Carbon emission evaluation method for waste incineration power generation

Abstract

The invention belongs to the field of data processing of industrial process supervision, and discloses a carbon emission evaluation method for waste incineration power generation, which comprises the following steps: synchronizing business management data and operation parameters of a process control system, establishing a background thermal power baseline model in a new material-free period, and establishing a background thermal power baseline model; the deviation integral of the actual thermal power relative to the baseline model after each time of feeding is calculated, an instantaneous energy response value is obtained, when signal superposition is caused by high-frequency feeding, unaliasing processing based on a self-learning response template is started to separate each time of response, and standard fuel is periodically injected to calibrate the baseline model; according to the method, continuous industrial data are segmented and attributed by using the discreteness characteristics of commercial events, and the corresponding relation between the batch of materials with specific sources and the real combustion performance in the furnace is reconstructed under the condition that the physical forms of the garbage are mixed, so that the technical problem of management information failure caused by production homogenization operation is solved.

Description

Technical Field

[0001] The invention relates to a carbon emission evaluation method for waste incineration power generation, belonging to the technical field of data processing for industrial process supervision. Background Art

[0002] Currently, in the operation and management of waste incineration power plants, distributed control systems or data acquisition and monitoring control systems are generally used to monitor the process flow of the incinerator boiler system. At the same time, enterprise resource planning systems are used to record the commercial information of waste entering the plant. This has become a way to ensure production stability and conduct commercial settlements. In order to ensure stable boiler combustion conditions and obtain continuous and stable steam output, a core operating principle at the operational level is to stir and mix all types of waste in the garbage pit for a long time to achieve short-term homogenization of the calorific value of the waste entering the furnace.

[0003] However, with the increasing demand for carbon emission management and refined cost accounting, management needs to establish an evaluation system that can directly link batches of garbage from a single source with their actual combustion quality in order to implement differentiated procurement strategies. At this time, an inherent constraint arises at the operational level: the physical homogenization operation that ensures stable production results in a lack of data correlation on the information level on which performance attribution depends. When garbage from different sources is physically mixed before entering the furnace, the properties of any garbage hoisted into the furnace become average values. This makes it impossible to logically attribute fluctuations in the back-end process control data to any specific batch of garbage source.

[0004] To address this issue, one approach is to add sampling equipment to analyze the physical or chemical composition of the waste before mixing. However, this approach is not only costly when dealing with large-scale, highly heterogeneous waste, but also inherently limited in its representativeness, making it difficult to meet the requirements of real-time, continuous, and economical online management. Therefore, the problem is not a lack of sensors, but rather an inherent contradiction within the existing technology system: necessary operational operations lead to the ineffectiveness of management information. Specifically, the existing technology suffers from the following major deficiencies: 1. The logical connection between performance attribution is broken, namely, the physical mixing operation in the production process blocks the direct correspondence between front-end business information and back-end process performance information; 2. The lag and indirectness of the evaluation method, namely, the evaluation of feed quality often relies on static data such as weighing at the time of entry, which cannot reflect its dynamic performance during the actual combustion process. Therefore, the technical problem to be solved by this invention is how to establish an instantaneous correspondence between a specific waste batch and the actual combustion performance in the furnace, without increasing additional hardware investment or changing the existing stable production process, and using only data from the existing process control system and business management system. Summary of the Invention

[0005] The present invention provides a carbon emission evaluation method for waste incineration power generation. Its main purpose is to solve the problem of how to use existing data to reconstruct the correspondence between waste batches that have been cut off by the physical mixing process and the actual combustion performance without changing the existing process and without adding new hardware.

[0006] To achieve the above objectives, the present invention provides a carbon emission evaluation method for waste incineration power generation, comprising the following steps:

[0007] Step S1: Synchronize the business management data, which records the timestamps and weights of waste batches, with the time series data of operational parameters representing the operating status of the incinerator-boiler system collected by the process control system, at the second level.

[0008] Step S2: During the combustion period when no new material is added, the real-time thermal power is calculated based on the main steam flow rate, main steam pressure and feed water flow rate in the operating parameters, and a background thermal power baseline model representing the stable combustion state of the system is established;

[0009] Step S3: For each waste batch loading, within a preset time window after the loading timestamp, calculate the deviation of the actual thermal power obtained by the operating parameters from the background thermal power baseline model, and integrate the deviation to obtain the instantaneous energy response value representing the energy contribution of the waste batch;

[0010] Step S4: When the interval between two consecutive waste batches is less than a preset threshold, a single batch response template based on learning from historical data is activated to perform sequence signal de-aliasing on the actual thermal power deviation formed by the superposition of multiple consecutive batches of waste, so as to separate the instantaneous energy response value attributable to each independent batch.

[0011] In step S5, baseline calibration is performed periodically. The baseline calibration involves injecting a preset amount of standard reference fuel with a known calorific value into the incinerator, and obtaining the measured reference response value using the calculation method of step S3. The calibration coefficient is then determined based on the measured reference response value and the theoretical calorific value of the standard reference fuel, and the calibration coefficient is used to adjust the background thermal power baseline model.

[0012] Preferably, the method also performs the following steps in parallel: when obtaining the operating parameters in step S1, an additional quick response parameter selected from the furnace outlet flue gas temperature and the furnace negative pressure is also obtained, and the response speed of the quick response parameter is faster than the operating parameter used to calculate the instantaneous energy response value; within the preset early warning monitoring window after each garbage batch is fed, the time change rate of the quick response parameter is calculated; and when the value of the time change rate exceeds the preset hazard threshold, a potential hazard warning associated with the garbage batch source information is generated, and the generation of the potential hazard warning is independent of the calculation process of the instantaneous energy response value.

[0013] Preferably, the calibration coefficient in step S5 is defined as the heat transfer efficiency factor , which is calculated as follows: ,in, is the theoretical reference response value that should be produced by the complete combustion of the standard reference fuel. is the measured reference response value; the adjustment of the background thermal power baseline model is to multiply the background thermal power baseline model as a whole by the heat conduction efficiency factor.

[0014] Preferably, the establishment of the single feeding response template in step S4 includes: automatically screening out isolated feeding events whose previous and subsequent feeding intervals are greater than a preset time window from historical business management data and operation parameter time series data; extracting the time series curve of the actual thermal power deviation corresponding to the isolated feeding event without aliasing; and performing time alignment and amplitude normalization processing on the extracted multiple time series curves, and performing averaging calculation to construct a single feeding response template that characterizes the standard response characteristics of the incinerator-boiler system.

[0015] Preferably, the sequence signal de-aliasing processing in step S4 is implemented by an iterative residual minimization algorithm. The algorithm optimizes and solves the instantaneous energy response value attributed to each feeding, so that the linear superposition of the single feeding response template of each independent feeding after scaling and time shifting the corresponding instantaneous energy response value and the actual thermal power deviation formed by the superposition of multiple consecutive feedings is minimized.

[0016] Preferably, the method also includes: calculating a combustion characteristic fingerprint characterizing the dynamic characteristics of combustion of the garbage batch based on the waveform form of the time series data of the deviation of the actual thermal power from the background thermal power baseline model attributable to each independent garbage batch feeding; and associating the combustion characteristic fingerprint with the source information of the garbage batch to form an evaluation of the combustion stability of batches from different sources.

[0017] Preferably, the calculation of the combustion characteristic fingerprint includes: performing amplitude normalization processing on the time series data of the actual thermal power deviation to eliminate the influence of the total energy size; and calculating the statistical characteristics of the time series data after the amplitude normalization processing, and the statistical characteristics are selected from the group consisting of at least one of the following items: the maximum slope of the rising section of the curve representing the energy release rate, the skewness representing the asymmetry of the energy release process, and the kurtosis representing the concentration of the energy release process.

[0018] Preferably, the preset time window in step S3 is 3 minutes to 5 minutes; the preset early warning monitoring window is 10 seconds to 30 seconds.

[0019] Preferably, the method further includes: binding the calculated instantaneous energy response value to the batch weight of the feeding; and as the number of feedings accumulates, automatically constructing an average effective calorific value portrait calculated based on multiple combustion data, and a variance portrait of the feeding quality stability.

[0020] Preferably, the method also includes: generating a comprehensive evaluation result of the carbon emission potential of garbage from a specific source based on one or more of the instantaneous energy response value, the average effective calorific value portrait, the frequency of occurrence of potential hazard warnings, and the combustion characteristic fingerprint, and using the comprehensive evaluation result for hierarchical management.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. Establish a new information association method. Starting from the timestamp of the waste batch feeding in the business management system, an analysis window with a clear cause and effect is defined in the continuous time series of boiler operating parameters recorded by the process control system. Within this window, by stripping off the background thermal power baseline under the stable combustion state and integrating the actual thermal power deviation caused by the batch feeding, a response value representing its instantaneous energy contribution is obtained. This process utilizes the discrete characteristics of business events to effectively segment and attribute continuous industrial production data. Even when the physical form of the waste has been evenly mixed and lost its distinguishability, the corresponding relationship between the material batch from a specific source and its actual combustion performance in the furnace can still be reconstructed.

[0023] 2. Construct a parallel monitoring and early warning process with complementary functions. While calculating the instantaneous energy response value by analyzing operating parameters such as main steam parameters with large thermal inertia and suitable for energy integration, it also monitors the change rate of parameters with faster response speed, such as furnace outlet flue gas temperature. When a feeding event occurs, if the flue gas temperature change rate exceeds the preset hazard threshold in a short period of time, the system will immediately generate a potential hazard warning associated with the source of the batch. At this time, the response process of the main steam parameters may not have been fully launched. The parallel processing of the two fast and slow parameter channels will expand the evaluation of the feeding batch from a single energy value dimension to the multi-dimensional management of combustion process risks and equipment impacts, so that cost accounting at the financial level and safety supervision at the production level can be carried out simultaneously.

[0024] 3. This method integrates a set of dynamic calibration and signal restoration mechanisms to ensure that its own evaluation benchmark can remain effective over the long term. It periodically injects standard reference fuel into the furnace and calculates the measured reference response value generated in the same way as the evaluation of garbage batches. Based on the relationship between this response value and the theoretical value, a calibration coefficient reflecting the current heat transfer efficiency of the system is determined to dynamically adjust the background thermal power baseline model. At the same time, when faced with a working condition where the continuous feeding interval is too short, resulting in the superposition of thermal response signals, the method will enable signal de-aliasing processing, that is, based on the single feeding response template learned from historical data, the instantaneous energy response value attributable to each independent feeding is separated from the superimposed signal. The combination of the two mechanisms of periodic baseline calibration and real-time restoration of the superimposed signal enables the evaluation results of this method to maintain their objectivity and consistency when dealing with two different time scales: the long-term slow decay of equipment performance and the short-term sharp changes in production rhythm. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a data processing and decision logic flow chart of a carbon emission assessment method of the present invention;

[0026] Figure 2 Schematic diagram showing the comparison of the effects before and after the dynamic calibration of the thermal power baseline according to the present invention;

[0027] Figure 3 This is a use case diagram of user roles and function interactions in an evaluation system of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] The present invention provides a carbon emission evaluation method for waste incineration power generation, which is applied to the operation and management of waste incineration power plants. Physical homogenization operations are usually required before the waste is put into the furnace. This process severs the direct correspondence between the commercial information of the front-end waste batch and its actual combustion process performance at the back-end. The method re-establishes the quantitative association between the material batch and its combustion performance in the furnace through a series of data processing steps without changing the existing incineration process. The method includes data synchronization, background thermal power baseline modeling instantaneous energy response calculation, de-aliasing processing of high-frequency feeding signal superposition, and periodic baseline model calibration. These steps process the data scattered in the enterprise resource planning system and process control system into data that can be used for evaluation results.

[0030] In a specific implementation environment, the execution of this method begins with step S1, i.e., the synchronization of business management data and process control system operating parameters. There may be deviations in the time basis between the garbage batch feeding timestamp recorded by the business management system and the boiler operating parameter time series data collected by the process control system. This method uses the network time protocol to synchronize the time of the server carrying the business management system and the process control system, and controls the error between the system time and the standard time source of the two to be within 100 milliseconds. On this basis, the data processing module synchronizes the business management data recorded with the garbage batch feeding timestamp and batch weight with the data characterizing the operation of the incinerator-boiler system collected by the process control system at a resolution of seconds. The time series data of the operating parameters of the state are aligned to form a unified data record with the feeding timestamp as the primary key and associated with the corresponding operating parameter sequence as the input for subsequent attribution calculations; then, the system executes step S2 to establish a background thermal power baseline model. This step first uses a deterministic logic to identify the combustion period without new material feeding. The logic is defined as: if no new garbage batch feeding timestamp is recorded in the business management data within 300 seconds before the current time point, it is determined that the system is in a stable combustion state; during the identified stable combustion period, the system calculates the real-time thermal power of the boiler based on the real-time collected main steam flow, main steam pressure and feed water flow. In one embodiment, the real-time thermal power (in megawatts) can be calculated by the formula Calculate, where is the main steam flow rate (in kg / s), is the steam enthalpy value corresponding to the main steam pressure (in kJ / kg), is the feed water enthalpy value at the corresponding feed water temperature (in kJ / kg); the system calculates the continuous A low-pass filter with a time constant of 180 seconds is applied to the value, and the filtered output is used as the background thermal power baseline model representing the stable combustion state of the system. , the model outputs , is used as a dynamic benchmark for calculating subsequent power deviations.

[0031] After obtaining the background thermal power baseline model, the system executes step S3 for each batch of garbage feeding to calculate the instantaneous energy response value. When the data processing module detects a new feeding timestamp When the system starts a preset time window, the length of the preset time window is set to 3 minutes to 5 minutes. In a preferred embodiment, the value is 240 seconds. During this window, the system calculates the actual thermal power. Relative to background thermal power baseline model Deviation ,in, for The actual thermal power at the moment, for The background thermal power baseline at the moment, for The power deviation at the moment, as time; and the deviation Integrate over the time interval to obtain the instantaneous energy response value that represents the energy contribution of the garbage batch , which is calculated as ;in, The feeding timestamp of the garbage batch feeding. For example, if after one feeding, within the 240-second window, the thermal power deviation integral calculation value is 120 megajoules, then the 120 megajoules will be recorded as the instantaneous energy response value of this feeding, and bound to the weight and source information of the batch, so that a commercial feeding event is associated with a quantified energy response value; when the interval between two consecutive garbage batch feedings is less than the preset time window, the thermal power deviation signal will be superimposed. At this time, the system activates step S4 and performs de-aliasing processing. The processing first needs to establish a single feeding response template. The establishment procedure is: automatically filter out isolated feeding events with a feeding interval of more than 300 seconds from the historical data, extract the non-aliased thermal power deviation time series curves corresponding to these events, and perform time alignment and amplitude normalization on at least 50 extracted curves, and then perform averaging calculation to construct a single feeding response template. In real-time operation, when the interval between two consecutive feedings is detected to be less than a preset threshold, such as 180 seconds, the system starts the de-aliasing process, which uses an iterative residual minimization algorithm to solve the instantaneous energy response value of each feeding. ,in Number the order of feeding in the high-frequency feeding sequence so that the single feeding response template of each independent feeding is The corresponding instantaneous energy response value Zoom and time shift The fitting error between the linear superposition after the addition and the actual thermal power deviation observed by the superposition of multiple consecutive feedings is minimized. The application of this algorithm makes it possible to separate the response values ​​attributable to each feeding under a high-frequency production rhythm.

[0032] In order to cope with the time-varying heat transfer efficiency of the boiler due to factors such as coking of the furnace tubes, the method further includes step S5, which periodically performs baseline calibration. The procedure is set to be automatically executed once every 24 hours. During execution, the system injects a preset amount of standard reference fuel with a known calorific value into the incinerator, for example, 10 kg of diesel with a calorific value of 42.7 MJ / kg; the system uses the same calculation method as step S3 to obtain the measured reference response value caused by the injection of the standard fuel. At the same time, the system stores the theoretical reference response value corresponding to the standard reference fuel Megajoules, the system then calculates the calibration factor, which is the heat transfer efficiency factor , which is calculated as follows: For example, if the actual value measured in this calibration is is 405.65 MJ, so we calculate ; Finally, the system will be the entire background thermal power baseline model Multiply the overall heat transfer efficiency factor Through this step, the evaluation results can be dynamically modified according to the current physical state of the system; when the system performs real-time thermal power calculation, the required main steam enthalpy value and feed water enthalpy , which is obtained by real-time query of the digital steam table function library built into the process control system and conforming to the International Association for the Properties of Water and Steam IAPWS-IF97 standard. The query takes the real-time pressure and temperature values ​​of the main steam and feed water collected at a frequency of seconds or higher as input; and when the sequence signal de-aliasing processing is enabled, the linear superposition model is adopted, that is, the actual thermal power deviation after superposition is regarded as the algebraic sum of the energy scaling and time shift of each independent feeding response template. Its engineering applicability is determined by the following calibration procedure: the total weight of homogeneous garbage fed N times (N≥3) in succession and rapid feeding is isolated and the instantaneous energy response integral value is recorded. , and then apply the de-aliasing algorithm to the N consecutive feeding events to obtain the instantaneous energy response values ​​after separation , if the sum and The relative deviation If the value is less than 5%, the model is deemed to be valid under the current working conditions of the system.

[0033] The present invention can also execute a risk management supervision and early warning process in parallel. When obtaining the operating parameters in step S1, the process additionally obtains a fast response parameter with a faster response speed than the main steam parameter, such as the furnace outlet flue gas temperature; after each batch of garbage is fed, the system opens an early warning monitoring window with a duration of 10 to 30 seconds. Within this window, the system calculates the time change rate of the furnace outlet flue gas temperature. ; When the value of the time change rate exceeds the preset hazard threshold, for example If the temperature exceeds 15 degrees Celsius per second, the system generates a potential hazard warning associated with the garbage batch. This process extends the evaluation of the feed batch to the risk dimension of the combustion process. It should be noted that this method can further calculate the combustion characteristic fingerprint. After obtaining the thermal power deviation time series data attributable to each independent feed, the amplitude is normalized and the statistical characteristics of the processed curve are calculated, such as the maximum slope, skewness and kurtosis of the rising section. These characteristics together constitute the combustion characteristic fingerprint and are associated with the garbage batch. As the number of feeds accumulates, the system can automatically construct an average effective calorific value portrait and a feed stability variance portrait, and use these portrait data, together with the frequency of potential hazard warnings and the combustion characteristic fingerprint, to generate a comprehensive evaluation result. Provide data support for differentiated procurement strategies; the instantaneous energy response value calculated for each independent feeding event is bound to and divided by the batch weight of the feeding to obtain the average effective calorific value that characterizes the energy density of the batch of materials. This calorific value and the combustion characteristic fingerprint data such as the kurtosis and asymmetric skewness that characterize the concentration of the energy release process, calculated from the waveform of the thermal power deviation curve, are stored in a database with a specific batch source; as the number of feedings accumulates, the system generates a quantitative evaluation of the energy potential and combustion stability of the feed based on the constructed average effective calorific value time series and the statistical distribution of the combustion characteristic fingerprint. This evaluation result is directly used as the input basis for subsequent adjustments to the garbage, thereby establishing a data association between the front-end commercial procurement decision and the back-end process combustion performance.

[0034] At the same time, to ensure the applicability and accuracy of the present method in different systems, the preset threshold in step S4 can be determined by statistically analyzing the thermal power deviation curves of multiple isolated feeding events used to construct a single feeding response template: specifically, the time elapsed for each curve to decay from reaching the peak to 10% of the peak is calculated, and the 80th percentile of the obtained time sample set is used as the preset threshold of the system, thereby objectively defining the time interval at which the thermal response signals begin to significantly overlap. In addition, in a preferred embodiment, the iterative residual minimization algorithm described in step S4 can be specifically implemented using the Levenberg-Marquardt algorithm (LM algorithm).

[0035] Example 1: In the management of a waste incineration power plant that is continuously operating at full capacity, the operations department performs long-term mixing of incoming waste to ensure stable boiler combustion conditions. This achieves short-term homogenization of the calorific value of the incoming materials, resulting in the physical properties of any batch of waste entering the furnace becoming an average value, making it impossible to obtain decision-making data directly related to combustion performance.

[0036] In this application scenario, the total amount of garbage supplied monthly by garbage generating units A and B is similar to the weight weighed upon entry into the factory. However, the records of the operation department show that during the processing of the garbage from unit B, the fluctuation frequency of the boiler main steam pressure and temperature increased slightly. Due to the lack of quantitative data, after the method of the present invention is deployed, the business management data recording the feeding timestamp and batch weight, as well as the operating parameters of the process control system characterizing the operating status of the incinerator-boiler system are synchronized at the second level in accordance with the regulations in the specific implementation method, and a dynamic background thermal power baseline model is established; within a cycle of system operation, when a garbage crane grabs a bucket of garbage from B and puts it into the furnace, the system records the feeding timestamp of the action, and the times after this timestamp. Within a preset 240-second time window, two data processing processes are executed in parallel. The first process calculates the actual thermal power based on parameters such as the main steam flow and pressure, and integrates it relative to the background thermal power baseline model to obtain the instantaneous energy response value of the batch; the second process monitors the time change rate of the fast-response parameter, the flue gas temperature at the furnace outlet, within a preset 30-second early warning monitoring window after feeding. The system continuously processed 100 feedings from B, and the calculation results were integrated into a feed source performance portrait containing two key indicators. The portrait shows that the average instantaneous energy response value of the batch from source B is 15% lower than that from A. At the same time, its potential hazard warning frequency is 22%, while for the batch from A with the same number of feedings, the frequency is only 3%.

[0037] The indicator of instantaneous energy response value provides a quantitative description of the insufficient energy contribution of feed B, while the frequency of potential hazard warnings provides a quantitative indicator of the impact of its feed on the stability of equipment operation. The management is based on this set of feed source portrait data including the two dimensions of energy contribution and combustion risk; the application of this method makes the homogenization operation of the operation department and the cost attribution target of the management department no longer conflict. The physical mixing process of the garbage pit remains, but the correspondence between the business information and process performance information that was originally cut off by this process is re-established through the processing of system response data. The technical approach to the problem has changed from how to distinguish the mixed garbage at the physical level to how to use discrete business event timestamps from the continuous system response data stream to decode the process performance corresponding to each business event.

[0038] Example 2: In order to verify the effectiveness of the method of the present invention in distinguishing batches of garbage with different combustion characteristics and conducting quantitative evaluation, the following experiment was conducted. The purpose of the experiment was to compare the evaluation results of the traditional evaluation method based only on weight with the evaluation results of the data processing method of the present invention under controlled conditions. The experiment was carried out on a garbage incinerator-boiler system with the same specifications as the application scenario. The process control system equipped with this system can collect and record operating parameters such as main steam flow, main steam pressure, feed water flow and furnace outlet flue gas temperature at a frequency of 1 Hz, and its measurement accuracy is ±0.5%, ±0.5%, ±0.5% and ±0.2 respectively. .

[0039] The experiment designed three standardized test batches, and the weight of each batch was controlled within the range of 1500kg±50kg to simulate the situation where batches from different sources have the same weight but different contents. The specific batch settings are as follows: Test batch A, composed of dry waste paper and waste plastic, simulates high calorific value and stable combustion materials, and its theoretical energy is about 18500 megajoules; test batch B, composed of mixed high-moisture kitchen waste, simulates low calorific value and stable combustion materials, and its theoretical energy is about 8200 megajoules; test batch C, composed of dry waste paper mixed with a small amount of industrial solvent waste, simulates materials with high total calorific value but unstable combustion, and its theoretical energy is similar to that of batch A, about 18300 megajoules. The experiment set up a control The control group was evaluated based on the batch weight recorded by the business management system. The experimental group used the complete method of the present invention to calculate the instantaneous energy response value and monitor the potential hazard warning. The experimental process was as follows: when the incinerator was in a stable combustion condition, the test batches A, B, and C were added 10 times continuously, and the interval between each feeding was set to be greater than 10 minutes to avoid the superposition of thermal response signals. For the control group, since the weight of each batch was within the error range, there was no significant difference in the evaluation results. For the experimental group, the system calculated the instantaneous energy response value for each feeding event in accordance with the specific implementation method and monitored the flue gas temperature change rate at the furnace outlet within 30 seconds after feeding. The core data of the experiment are recorded in Table 1.

[0040] Table 1: Comparison of evaluation results of different test batches; Trial batch type Batch weight (kg) Theoretical energy (MJ) Control group evaluation Test group-average instantaneous energy response value (MJ) Experimental group - number of potential hazard warnings (10 times in total) A 1521 18500 No difference 18455 0 B 1488 8200 No difference 8270 0 C 1505 18300 No difference 18240 8 .

[0041] As shown in Table 1, the average instantaneous energy response value calculated by the test group is correlated with the theoretical energy of each batch. The response values ​​of batches A and C are both above 18,200 MJ, while the response value of batch B is around 8,300 MJ. This shows that the calculation of the instantaneous energy response value can reflect the energy contribution of the material. For batches A and C with similar theoretical energy, the evaluation results of the test group diverged. Eight out of ten feedings of batch C triggered potential hazard warnings, while batch A did not have any warnings. The reason is that the industrial solvent waste in batch C caused a large amount of heat to be released instantaneously during its combustion process, causing the flue gas temperature change rate at the furnace outlet to exceed the preset hazard threshold. This dynamic process cannot be fully characterized by the instantaneous energy response value based on energy integration. The test results show that the method of the present invention can generate an evaluation result including two dimensions, energy contribution and combustion stability, by processing energy-related parameters and risk-related parameters in parallel. This result can distinguish between batches of materials with similar weight but different combustion characteristics, thereby providing a basis for material combustion performance evaluation. Refined feeding management decisions provide data support that cannot be obtained by weight data alone. It should be noted that in order to further quantitatively describe the differences in the combustion dynamics between test batches A and C in this embodiment, the method of the present invention also calculates the combustion characteristic fingerprints of the feeding events that did not trigger potential hazard warnings in the two tests. During the calculation, the time series data of the actual thermal power deviation was first normalized, and then the skewness and kurtosis statistical characteristics were calculated. The results showed that the combustion characteristic fingerprint of test batch A showed a skewness close to 0 and a kurtosis value less than 3, while the combustion characteristic fingerprint of test batch C showed a skewness greater than 1.5 and a kurtosis value greater than 5. This data shows that even in combustion events that did not exceed the hazard threshold, the energy release process of batch C showed asymmetry and short-term concentration. By associating the combustion characteristic fingerprint with batch source information, the management system can classify the combustion process stability of different feeds at a finer granularity, thereby identifying those material batches that did not cause severe impact but still had unstable combustion quality.

[0042] Example 3: This example combines Figures 1 to 3 , a carbon emission evaluation method for waste incineration power generation is described, such as Figure 1As shown in FIG, the process starts with obtaining continuous time series data such as main steam flow, pressure and furnace flue gas temperature from the process control system, and obtaining discrete event data including garbage batch feeding timestamp and batch weight from the business management data. Step S1 synchronizes and aligns the data from these two sources at the second level. Step S2 establishes a background thermal power baseline model that characterizes the stable combustion state of the system by identifying the period without new material, which serves as a dynamic evaluation benchmark. Step S3 calculates the deviation of the actual thermal power from the baseline model for each feeding event, and quantifies the instantaneous energy contribution of this feeding by integrating the deviation to obtain the instantaneous energy response value. When the system determines that high-frequency feeding causes the thermal power response signal to be superimposed, step S4 is enabled. This step is based on the historical data. The single-batch feeding response template established through self-learning in the data performs serial signal de-aliasing processing on the superimposed signal to separate the responses attributable to each independent feeding. If there is no signal superposition, this step is skipped. At the same time, a parallel potential hazard warning process is independent of the energy calculation, and the combustion process risks are identified by monitoring the rate of change of fast-response parameters such as the flue gas temperature at the furnace outlet. In addition, step S5 serves as a periodic feedback calibration link. By injecting standard fuel into the furnace, the heat transfer efficiency factor is calculated and used to dynamically adjust the background thermal power baseline model established in step S2. Finally, the method integrates the processing results of each step to generate an average effective calorific value portrait, combustion stability and risk warning evaluation, providing decision support for the hierarchical management of feeding batches and the optimization of the combustion process.

[0043] like Figure 2 As shown in the figure, the horizontal axis is time in hours, and the vertical axis is thermal power in MW. The figure contains three curves, among which the dotted line marked as the baseline before calibration represents a lower constant power reference value used by the system before calibration. The broken line with data points marked as actual thermal power represents the actual fluctuating thermal power output of the system within 24 hours. The thick solid line marked as the baseline after calibration is calculated and adjusted according to the measured system heat transfer efficiency after the periodic calibration procedure is executed. It is an average power benchmark that is closer to the actual operating conditions of the current period. By dynamically adjusting the evaluation benchmark from the baseline before calibration to the baseline after calibration, it is ensured that the calculation of the instantaneous energy response value can eliminate the systematic error caused by the long-term performance degradation of the equipment, thereby maintaining the objectivity and consistency of the evaluation results.

[0044] like Figure 3As shown in the figure, two core participant roles are defined, namely the management and decision-making layer and the operation department. The operation department is responsible for performing periodic baseline calibration and receiving potential hazard warnings issued by the system. The data required for its operation, such as real-time operation parameters, are provided by the process control system. The management and decision-making layer mainly uses the core function of this method, namely generating a comprehensive evaluation. The generation process of this comprehensive evaluation relies on a series of sub-functions such as calculating instantaneous energy response, analyzing combustion characteristic fingerprints, and constructing feed source portraits. The basic data required for these functions, such as feeding event data, are provided by the business management system. The figure defines the responsibilities of users at different levels and the interactive relationship between each functional module within the method and the external data system.

[0045] Example 4: This example provides a calibration procedure for determining specific parameters in the evaluation method of the present invention, which is applied to the initialization configuration when the method is deployed in a specific incinerator-boiler system. The configuration process involves the determination of two parameters: a preset time window and a hazard threshold. To determine the value of the preset time window, 100 isolated feeding events are first screened out from the historical data collected after the system has been running stably. An isolated feeding event is defined here as an event in which there are no other feeding records within 600 seconds before and after the feeding timestamp. For each isolated feeding event, the corresponding time series curve of the actual thermal power deviation is extracted, and the energy cumulative integral curve of the deviation curve starting from the feeding timestamp is calculated. Subsequently, for each energy cumulative integral curve, the time it takes to reach 95% of the final integral value is calculated, which is recorded as After calculating 100 isolated feeding events, a The sample set of values ​​has a 90th percentile of 238.4 seconds. Based on this statistical result, the preset time window for this specific system is set to 240 seconds.

[0046] In order to determine the hazard threshold of the furnace outlet flue gas temperature change rate, the system's operating data from the past quarter was used. This data included several unstable events marked by material problems that caused furnace operating conditions to fluctuate, as well as stable operating data for a large number of time periods. The data processing program calculated the time change rate of the furnace outlet flue gas temperature in all historical data. Then, these change rate values ​​are divided into two groups, one group comes from the stable operation period, and the other group comes from the 5-minute period before and after the unstable event. The data of the stable operation group are statistically analyzed. The 99.9th percentile is 12.5 / second, and statistics on the unstable event group showed that more than 85% of the events had Peak value greater than 15 / second, based on this statistical analysis, the hazard threshold of this system is set to 15 / second; by executing the above two calibration procedures, the evaluation method applied to this specific incinerator-boiler system, its preset time window and hazard threshold are determined by the operating data of the system.

[0047] Example 5: When the evaluation method is applied to an incineration plant that has been in a high-feeding frequency operating condition for a long time, resulting in a lack of isolated feeding events in historical data, before enabling the serial signal de-aliasing processing function, a template calibration procedure is executed. The procedure is performed during a predetermined low-load operating period, during which 20 batches of standard weight garbage are fed, and the interval time between each feeding is controlled to be more than 600 seconds, thereby generating a set of isolated feeding events without signal aliasing for template construction.

[0048] The system collects the actual thermal power deviation time series curve corresponding to these 20 feeding events, and constructs a single feeding response template for this incineration plant according to the above steps. To verify the applicability of the template, the system performs a test including three consecutive rapid feedings with an interval of 60 seconds. After applying the newly generated template to perform sequence signal de-aliasing processing, the instantaneous energy response value attributable to the three feedings is obtained. 、 and , the sum of Compared with the instantaneous energy response value measured by a single isolated charge of the total weight of three charges of homogeneous garbage, the deviation between the two is less than 5%. This calibration procedure provides a technical path for the system to obtain a single charge response template in the absence of historical isolated charge event data.

[0049] Example 6: This example provides an engineering procedure for establishing a reference value for the periodic baseline calibration function in the method of the present invention. The procedure is executed when the evaluation method is first deployed in an incinerator-boiler system, or after the system completes a planned overhaul aimed at restoring the cleanliness of the heat exchange surface. Its purpose is to establish a theoretical reference response value for the system in a clean state for subsequent calculation of the heat transfer efficiency factor; the procedure is started after the incinerator-boiler system completes the heat exchange surface cleaning treatment, and the system is stably operated at 70% of the rated load and maintained at this operating condition for not less than 2 hours to allow the system thermal parameters to reach equilibrium. Thereafter, the calibration process begins, and the auxiliary fuel spray gun is used to continuously inject 10 standard reference fuels into the furnace, each time injecting 10 kilograms of diesel, and the interval between two consecutive injection operations is controlled to be not less than 10 minutes, so that the system can recover from the response of the last injection to a stable background thermal power baseline.

[0050] For these 10 independent standard reference fuel injection events, the evaluation method system uses the same calculation method as step S3 to calculate the measured reference response value caused by each event, and obtains a sample set containing 10 measurement values ​​{ }, and the arithmetic mean of these 10 measurements is used as the theoretical reference response value of the system in this clean state , and its calculation formula is ,in, The serial number representing each independent measurement operation. If the average value of 10 measurements is 428.5 MJ, this value is fixed and stored in the system configuration file. After this procedure is completed, the system obtains a theoretical reference response value based on multiple actual measurements and statistical averaging under the clean state of this specific system. , which is then used as the heat transfer efficiency factor The reference is used in all subsequent periodic baseline calibration processes.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A carbon emission evaluation method for waste incineration power generation, characterized in that: The steps include: Step S1: Synchronize the business management data that records the timestamps and weights of waste batches with the time series data of operating parameters that characterize the operating status of the incinerator-boiler system collected by the process control system; Step S2: During the combustion period when no new material is added, the real-time thermal power is calculated based on the main steam flow rate, main steam pressure and feed water flow rate in the operating parameters, and a background thermal power baseline model representing the stable combustion state of the system is established; Step S3: For each waste batch loading, within a preset time window after the loading timestamp, calculate the deviation of the actual thermal power obtained by the operating parameters from the background thermal power baseline model, and integrate the deviation to obtain the instantaneous energy response value representing the energy contribution of the waste batch; Step S4: When the interval between two consecutive waste batches is less than a preset threshold, a single batch response template based on learning from historical data is activated to perform sequence signal de-aliasing on the actual thermal power deviation formed by the superposition of multiple consecutive batches of waste, so as to separate the instantaneous energy response value attributable to each independent batch. In step S5, baseline calibration is performed periodically. The baseline calibration involves injecting a preset amount of standard reference fuel with a known calorific value into the incinerator, and obtaining the measured reference response value using the calculation method of step S3. The calibration coefficient is then determined based on the measured reference response value and the theoretical calorific value of the standard reference fuel, and the calibration coefficient is used to adjust the background thermal power baseline model.

2. A carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The method also performs the following steps in parallel: when obtaining the operating parameters in step S1, an additional quick response parameter selected from the furnace outlet flue gas temperature and the furnace negative pressure is also obtained, and the response speed of the quick response parameter is faster than the operating parameter used to calculate the instantaneous energy response value; within the preset early warning monitoring window after each batch of garbage is added, the time change rate of the quick response parameter is calculated; and when the value of the time change rate exceeds the preset hazard threshold, an information-related potential hazard warning is generated, and the generation of the potential hazard warning is independent of the calculation process of the instantaneous energy response value.

3. The carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The calibration coefficient in step S5 is defined as the heat transfer efficiency factor , which is calculated as follows: ,in, is the theoretical reference response value that should be produced by the complete combustion of the standard reference fuel. is the measured reference response value; the adjustment of the background thermal power baseline model is to multiply the background thermal power baseline model as a whole by the heat conduction efficiency factor.

4. The carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The establishment of a single feeding response template in step S4 includes: automatically screening out isolated feeding events whose intervals between the previous and subsequent feedings are greater than a preset time window from historical business management data and operation parameter time series data; extracting a time series curve of actual thermal power deviation without aliasing corresponding to the isolated feeding event; and performing time alignment and amplitude normalization processing on the multiple extracted time series curves, and performing averaging calculation.

5. The carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The sequence signal de-aliasing processing in step S4 is implemented by an iterative residual minimization algorithm. The algorithm optimizes and solves the instantaneous energy response value attributed to each feeding, so that the linear superposition of the single feeding response template of each independent feeding after scaling and time shifting the corresponding instantaneous energy response value and the actual thermal power deviation formed by the superposition of multiple consecutive feedings is minimized.

6. The carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The method also includes: calculating a combustion characteristic fingerprint characterizing the dynamic combustion characteristics of the garbage batch based on the waveform form of the time series data of the deviation of the actual thermal power from the background thermal power baseline model attributable to each independent garbage batch feeding; and associating the combustion characteristic fingerprint with the source information of the garbage batch.

7. A carbon emission evaluation method for waste incineration power generation according to claim 6, characterized in that: The calculation of the combustion characteristic fingerprint includes: performing amplitude normalization processing on the time series data of the actual thermal power deviation; and calculating the statistical characteristics of the time series data after the amplitude normalization processing, wherein the statistical characteristics are selected from the group consisting of at least one of the following items: the maximum slope of the rising section of the curve representing the energy release rate, the skewness representing the asymmetry of the energy release process, and the kurtosis representing the concentration of the energy release process.

8. The carbon emission evaluation method for waste incineration power generation according to claim 2, characterized in that: The preset time window in step S3 is 3 minutes to 5 minutes; the preset early warning monitoring window is 10 seconds to 30 seconds.

9. The carbon emission evaluation method for waste incineration power generation according to claim 1, characterized in that: The method also includes: binding the calculated instantaneous energy response value to the batch weight of the material being fed; and automatically constructing an average effective calorific value portrait based on multiple combustion data calculations as the number of feedings accumulates.

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