Big data-based cement industry pollution reduction and carbon reduction synergy degree analysis method and system

Abstract

The application discloses a cement industry pollution reduction and carbon reduction synergy degree analysis method and system based on big data, and relates to the technical field of pollution reduction and carbon reduction analysis. Production process parameters of cement are collected, parameter fluctuation analysis is performed on the production process parameters, production event types are obtained, emission identification is performed on the production process parameters of cement according to the production event types, emission change results corresponding to the production event types are obtained, a pollution reduction and carbon reduction production response atlas is constructed according to the emission change results, time offset processing is performed on the production process parameters according to the pollution reduction and carbon reduction production response atlas, and then a collaborative change sequence is constructed, pollution reduction and carbon reduction synergy degree analysis is performed based on the collaborative change sequence, and collaborative emission reduction analysis results of the cement production process can be obtained, so that the synergistic effect of pollution reduction and carbon reduction can be accurately quantified, and reliable decision-making basis can be provided for the collaborative emission reduction regulation and control of the cement production process.

Description

Technical Field

[0001] This invention relates to the field of pollution reduction and carbon reduction analysis technology, specifically to a method and system for analyzing the synergy of pollution reduction and carbon reduction in the cement industry based on big data. Background Technology

[0002] In cement production, clinker calcination is completed through a rotary kiln system, which exhibits significant thermal inertia and material transport lag. Accurate assessment of the synergy between pollution reduction and carbon reduction is a key technological support for achieving the green and low-carbon transformation of the cement industry. Its core task is to construct a synergy analysis method based on multi-source production process parameters such as kiln operation data and flue gas emission data, which can truly reflect the relationship between pollutant emissions and carbon emission changes, providing a scientific basis for production control.

[0003] During the calcination of cement clinker, there are significant differences in the response time of different emission indicators. When production control parameters change, changes in combustion conditions will affect nitrogen oxide emissions in a short period of time, while changes in carbon dioxide emissions from carbonate decomposition often require the material to be transported and reacted within the kiln before gradually manifesting. This difference in response time stems from the physicochemical nature of the production process: nitrogen oxide generation is mainly affected by instantaneous conditions such as combustion temperature and oxygen content, resulting in a rapid response; while carbon dioxide emissions are closely related to process parameters such as raw material decomposition rate and material residence time, and are subject to the combined effects of thermal inertia and material transport lag, resulting in a slower response. When control measures are implemented, some pollutant indicators have already changed, while carbon emissions have not yet responded, leading to a time-distributed misalignment of the effects of the same control measures on different emission indicators.

[0004] If pollutant emission and carbon emission data are statistically calculated directly within a unified time window during synergy analysis, this temporal misalignment can lead to severe analytical distortion. When pollutants decrease but carbon emissions have not yet responded, the system may misjudge it as a decrease in pollutants but an increase in carbon emissions, thus arriving at an incorrect negative synergy conclusion. Conversely, when pollutants increase but carbon emissions have not yet responded, the system may misjudge it as a positive synergy. This distortion in synergy calculation caused by production cycle misalignment not only fails to accurately reflect the actual effectiveness of control measures but may also mislead production regulation, leading to the abandonment of potentially effective pollution reduction and carbon reduction measures or the adoption of ineffective control schemes, severely impacting the green and low-carbon transformation process of the cement industry. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for analyzing the synergy between pollution reduction and carbon reduction in the cement industry based on big data, which can effectively solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solution: a method for analyzing the synergy of pollution reduction and carbon reduction in the cement industry based on big data, including collecting cement production process parameters, performing parameter fluctuation analysis on the production process parameters, and obtaining production event types.

[0007] Based on the type of production event, emissions are identified for cement production process parameters to obtain the emission change results corresponding to the type of production event.

[0008] Based on the emission change results corresponding to the types of production events, a pollution reduction and carbon reduction production response map is constructed.

[0009] Based on the pollution reduction and carbon reduction production response map, the production process parameters are processed by time offset, and then a synergistic change sequence is constructed. Based on the synergistic change sequence, the pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process.

[0010] Furthermore, the cement production process parameters include kiln operation data, energy consumption data, material operation and reaction parameters, and flue gas emission data.

[0011] Furthermore, the method for performing parameter fluctuation analysis on production process parameters is as follows: The kiln operation data, energy consumption data, material operation reaction parameters, and flue gas emission data are arranged sequentially according to a unified time sampling period to construct a continuous time series, and a sliding analysis window is formed in the continuous time series with five sampling periods.

[0012] The production event types are obtained by jointly analyzing the various production process parameters within the sliding analysis window. Production event types include the raw material preheating stage, the pre-decomposition stage in the decomposer, the rotary kiln firing stage, the clinker cooling stage, and the stable operation stage of the kiln system.

[0013] Furthermore, the method for obtaining the emission change results corresponding to the production event type is as follows: Based on the identification of production event types, continuous change scanning processing is performed on the flue gas emission data within the time interval of each production event type. During the continuous change scanning process, emission change initiation stage identification processing and emission change duration stage identification processing are performed on the flue gas emission data within the sliding analysis window to obtain the emission change stage type.

[0014] The types of emission change phases include the initial emission change phase and the sustained emission change phase.

[0015] The start time and duration of emission changes are determined by analyzing the initial and sustained phases of emission changes.

[0016] Based on flue gas emission data, the magnitude of emission changes was analyzed.

[0017] The emission change start time, emission change duration, emission change magnitude, and emission change stage type are encapsulated into emission change results corresponding to the production event type.

[0018] Furthermore, the method for constructing the pollution reduction and carbon reduction production response map is as follows: Based on the emission change results corresponding to each production event type, the start time of emission changes of each emission indicator in the flue gas emission data under the same production event type is sorted and statistically analyzed to obtain the response order of each emission indicator under that production event type.

[0019] By statistically analyzing the time difference between the start time of emission changes of each emission indicator and the start time of the production event under the same type of production event, the response time interval of each emission indicator relative to the production event is obtained.

[0020] By structurally associating the response order and response time interval of various emission indicators under the same production event type, a pollution reduction and carbon reduction production response map is constructed.

[0021] The pollution reduction and carbon reduction production response map uses production event type as an index to record the response order and response time interval of each emission indicator under that production event type.

[0022] Furthermore, the method for time-shifting production process parameters based on the pollution reduction and carbon reduction production response map is as follows: Based on the response order and response time interval of each emission indicator corresponding to the production event type recorded in the pollution reduction and carbon reduction production response map, the flue gas emission data is time-shifted to align the emission change stages of carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration corresponding to the same production event type.

[0023] Furthermore, the method for constructing the cooperative change sequence is as follows: After completing the time offset processing, the time-aligned flue gas emission data are arranged in chronological order to construct a coordinated change sequence.

[0024] In the coordinated change sequence, the direction of change of each emission indicator within each sliding analysis window is identified. When at least three of the emission indicators, namely carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration, continuously decrease within three consecutive sliding analysis windows and the decrease exceeds their respective preset decrease thresholds, the corresponding time interval is marked as the multi-indicator coordinated decrease stage.

[0025] When the carbon dioxide concentration continues to decrease and the decrease exceeds the preset decrease threshold, while two or more emission indicators among the nitrogen oxide concentration, sulfur dioxide concentration, and flue gas particulate matter concentration continue to increase and the increase exceeds their respective preset increase thresholds, and remain at this level for three consecutive sliding analysis windows, the corresponding time interval is marked as a multi-indicator reverse change stage, resulting in a coordinated change sequence that includes the coordinated change stage marker.

[0026] Furthermore, the method for performing pollution reduction and carbon reduction synergy analysis based on synergistic change sequences is as follows: Statistical analysis was performed on the multi-indicator coordinated decline phase and the multi-indicator reverse change phase in the coordinated change sequence to calculate the comprehensive coordinated emission reduction degree corresponding to each production event type.

[0027] In the coordinated change sequence, for each time interval marked as a multi-indicator coordinated decline phase, the comprehensive coordinated emission reduction of the multi-indicator coordinated decline phase is identified.

[0028] For each time interval marked as a multi-indicator inverse change phase, the comprehensive coordinated emission reduction of the multi-indicator inverse change phase is identified.

[0029] The overall coordinated emission reduction for all time intervals under the same production event type is summed to obtain the overall coordinated emission reduction degree for that production event type.

[0030] Based on the comprehensive synergistic emission reduction degree of each production event type, the synergistic emission reduction analysis characterization value of the cement production process is obtained.

[0031] Furthermore, the method for obtaining the synergistic emission reduction analysis results of the cement production process is as follows: Based on the characterization values ​​of synergistic emission reduction analysis of the cement production process, the results of synergistic emission reduction analysis of the cement production process are obtained.

[0032] The synergistic emission reduction analysis characterization value of the cement production process is compared with the preset synergistic emission reduction analysis characterization threshold. If the synergistic emission reduction analysis characterization value of the cement production process is higher than the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process is marked as having a good synergistic effect of pollution reduction and carbon reduction.

[0033] If the synergistic emission reduction analysis characterization value of the cement production process is lower than or equal to the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process will be marked as insufficient synergistic effect of pollution reduction and carbon reduction.

[0034] The second aspect of this invention provides a big data-based system for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry, comprising: The event type analysis module is used to collect cement production process parameters, perform parameter fluctuation analysis on the production process parameters, and obtain the production event types.

[0035] The emission identification module is used to identify emissions from cement production process parameters based on the type of production event, and obtain the emission change results corresponding to the type of production event.

[0036] The graph construction module is used to construct pollution reduction and carbon reduction production response graphs based on the emission change results corresponding to the production event types.

[0037] The synergy analysis module is used to perform time offset processing on the production process parameters based on the pollution reduction and carbon reduction production response map, thereby constructing a synergistic change sequence. Based on the synergistic change sequence, pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process.

[0038] The present invention has the following beneficial effects: This invention analyzes parameter fluctuations in the cement production process and identifies production event types such as the raw material preheating stage and the pre-decomposition stage in the decomposition furnace, enabling emission change analysis to establish a correspondence with specific production process stages. By structurally linking production operation status with emission change processes, emission changes can be identified and statistically analyzed within the context of specific production events, thereby improving the accuracy and interpretability of emission change identification and enhancing the guidance capability of pollution reduction and carbon reduction analysis results for actual production control.

[0039] This invention constructs a pollution reduction and carbon reduction production response map by sorting and statistically analyzing the start times of emission changes for different emission indicators and calculating the response time interval of each emission indicator relative to production events. Based on this, time-shifting processing is applied to flue gas emission data according to the response map, aligning the time stages of changes in emission indicators such as carbon dioxide concentration, sulfur dioxide concentration, and particulate matter concentration. This eliminates the time-series misalignment problem between different emission indicators at the data analysis level, effectively avoiding misjudgments of synergy caused by response lags, thereby significantly improving the authenticity and reliability of the pollution reduction and carbon reduction synergy analysis results.

[0040] After aligning emission data over time, this invention constructs a synergistic change sequence and identifies the synergistic decline phase and the inverse change phase of multiple indicators. Furthermore, it employs a weighted analysis method to comprehensively quantify the changes in each emission indicator. This quantitatively reflects the degree of synergistic change between pollutant emissions and carbon emissions, identifies the trade-off between carbon emission reduction and pollutant emission increase, thereby improving the precision of the evaluation of the synergy between pollution reduction and carbon reduction. This provides more accurate and systematic technical support for the optimization control of cement production processes and energy conservation and emission reduction decisions. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0042] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: a method for analyzing the synergy of pollution reduction and carbon reduction in the cement industry based on big data, including collecting cement production process parameters, performing parameter fluctuation analysis on the production process parameters, and obtaining production event types.

[0045] By uniformly collecting and analyzing the fluctuations of production process parameters, complex continuous production processes can be divided into production event stages with clear technological characteristics. This allows emission changes to be directly correlated with specific production activities, thereby avoiding analytical biases caused by directly collecting emission data throughout the entire production cycle and improving the relevance and accuracy of pollution reduction and carbon reduction synergy analysis.

[0046] The cement production process parameters include kiln operation data, energy consumption data, material operation and reaction parameters, and flue gas emission data.

[0047] Kiln operation data includes rotary kiln speed, kiln head temperature, kiln tail flue gas temperature, decomposition furnace temperature, kiln tail flue gas oxygen content, and grate cooler air volume.

[0048] It should be added that the rotary kiln rotation speed refers to the speed at which the kiln body rotates, reflecting the movement and residence time of the material inside the kiln. This speed is measured in real-time by a speed sensor installed on the kiln drive unit. The kiln head temperature refers to the flue gas temperature in the kiln head region, reflecting the thermal intensity of the firing zone. This temperature is continuously measured by a thermocouple installed at the kiln head. The kiln tail flue gas temperature refers to the flue gas temperature at the kiln tail outlet, reflecting the heat exchange effect during the preheating and decomposition stages. This temperature is continuously measured by a thermocouple installed in the kiln tail flue gas chamber. The decomposition furnace temperature... The temperature refers to the flue gas temperature inside the decomposition furnace, reflecting the extent of the carbonate decomposition reaction. The decomposition furnace temperature is continuously measured by thermocouples installed inside the furnace. The oxygen content of the kiln tail flue gas refers to the volume percentage concentration of oxygen in the flue gas at the kiln tail outlet, reflecting the degree of excess air in the kiln combustion. The oxygen content of the kiln tail flue gas is continuously measured by a zirconia oxygen analyzer installed in the kiln tail flue. The grate cooler airflow refers to the flow rate of cooling air blown into the clinker through the grate cooler, reflecting the cooling intensity of the clinker. The grate cooler airflow is continuously measured by a flow meter installed in the air inlet duct of the grate cooler.

[0049] Energy consumption data includes the amount of pulverized coal input.

[0050] It should be added that the coal powder input refers to the mass of coal powder injected into the decomposition furnace and kiln head per unit time, reflecting the level of fuel input. The coal powder input is continuously measured by a solid flow meter installed on the coal powder conveying pipeline.

[0051] Material operation reaction parameters include raw material decomposition rate and clinker temperature.

[0052] It should be added that the raw meal decomposition rate refers to the proportion of carbonates that have been decomposed in the raw meal, reflecting the efficiency of the preheating decomposition system. The raw meal decomposition rate is calculated by taking samples at the outlet of the decomposition furnace and performing chemical analysis of loss on ignition. The clinker temperature refers to the temperature of the clinker in the kiln, reflecting the clinker cooling effect. The clinker temperature is obtained by continuously measuring it with an infrared thermometer installed at the outlet of the grate cooler.

[0053] The flue gas emission data includes carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration, particulate matter concentration, and flue gas flow rate.

[0054] It should be added that carbon dioxide concentration refers to the volume percentage or mass concentration of carbon dioxide in flue gas, reflecting the carbon emission level from carbonate decomposition and fuel combustion. It is continuously measured using a non-dispersive infrared absorption gas analyzer installed in the kiln tail flue. Nitrogen oxide concentration refers to the mass concentration of nitrogen oxides in flue gas, reflecting the level of thermal nitrogen oxide formation during combustion. It is continuously measured using a chemiluminescence or non-dispersive infrared absorption gas analyzer installed in the kiln tail flue. Sulfur dioxide concentration refers to the mass concentration of sulfur dioxide in flue gas, reflecting the sulfur release level from raw materials and fuels. The sulfur dioxide concentration is continuously measured by a gas analyzer using ultraviolet fluorescence or non-dispersive infrared absorption methods installed in the kiln tail flue. The particulate matter concentration refers to the mass concentration of suspended particulate matter in the flue gas, reflecting the dust emission level. It is continuously measured by a particulate matter monitor using laser scattering or beta-ray methods installed in the kiln tail flue. The flue gas flow rate refers to the volume of flue gas passing through the flue per unit time. The instantaneous flue gas flow rate is obtained by continuously measuring the flue gas velocity using a Pitot tube flow meter or ultrasonic flow meter installed in the kiln tail flue and combining it with the cross-sectional area of ​​the flue. The flue gas flow rate is then calculated by integrating the instantaneous flow rate within the measurement period.

[0055] Production event types include the raw material preheating stage, the pre-decomposition stage in the decomposer, the rotary kiln firing stage, the clinker cooling stage, and the stable operation stage of the kiln system.

[0056] The method for analyzing parameter fluctuations in the production process is as follows: The kiln operation data, energy consumption data, material operation reaction parameters, and flue gas emission data are arranged sequentially according to a unified time sampling period to construct a continuous time series, and a sliding analysis window is formed in the continuous time series with five sampling periods.

[0057] By using a sliding window to jointly analyze the fluctuation characteristics of multi-source parameters, it is possible to accurately delineate the various process stages of cement production. This stage division based on the physical meaning of the process lays the foundation for subsequent analysis of the emission response sequence, because different stages have different thermal inertia and material transport characteristics, resulting in significant differences in the response time of emission indicators. For example, in the raw material preheating stage, carbon dioxide emissions have not yet been generated in large quantities, while nitrogen oxides may have already begun to fluctuate due to changes in combustion conditions. By accurately identifying the stages, the response patterns within each stage can be analyzed in a targeted manner, avoiding the introduction of cascading effects from different stages into the synergistic evaluation.

[0058] After the continuous time series is constructed, temperature fluctuation identification processing is performed on the kiln operation data within the sliding analysis window. When the kiln tail flue gas temperature is within the set preheating range and the decomposition furnace temperature is within the set preheating range, and the raw material decomposition rate in the material operation reaction parameters is lower than the set low decomposition rate threshold and the carbon dioxide concentration in the flue gas emission data is lower than the set low concentration threshold, and this state is maintained within three continuous sliding analysis windows, the corresponding time interval is determined to be the raw material preheating stage, and the production event type is marked as the raw material preheating stage.

[0059] Within the sliding analysis window, decomposition reaction identification processing is performed. When the decomposition furnace temperature is in the set preheating range and the raw material decomposition rate in the material operation reaction parameters is in the set decomposition range, while the carbon dioxide concentration in the flue gas emission data is higher than the set decomposition concentration threshold and the oxygen content in the kiln tail flue gas is in the set low oxygen range, and this state is maintained within three consecutive sliding analysis windows, the corresponding time interval is determined to be the decomposition furnace pre-decomposition stage, and the production event type is marked as the decomposition furnace pre-decomposition stage.

[0060] The firing reaction identification process is performed within the sliding analysis window. When the kiln head temperature is within the set firing range and the rotary kiln speed is within the set speed range, the pulverized coal input is within the set high load range and the nitrogen oxide concentration in the flue gas emission data is within the set high concentration range, and this state is maintained within three consecutive sliding analysis windows, the corresponding time interval is determined to be the rotary kiln firing stage, and the production event type is marked as the rotary kiln firing stage.

[0061] Within the sliding analysis window, the clinker cooling identification process is performed. When the clinker temperature in the material operation reaction parameters is in the set cooling range, the grate cooler air volume in the kiln operation data is in the set high air volume range, and the flue gas flow rate in the flue gas emission data is in the set high flow rate range, and this state is maintained within three consecutive sliding analysis windows, the corresponding time interval is determined to be the clinker cooling stage, and the production event type is marked as the clinker cooling stage.

[0062] Within the sliding analysis window, stable operation identification is performed. When the kiln head temperature does not exceed the set temperature fluctuation threshold within three consecutive sliding analysis windows, and the coal powder input in the energy consumption data does not exceed the set load fluctuation threshold, while the oxygen content in the kiln tail flue gas does not exceed the set oxygen content fluctuation threshold, and this state is maintained within six consecutive sliding analysis windows, the corresponding time interval is determined to be the stable operation stage of the kiln system, and the production event type is marked as the stable operation stage of the kiln system for subsequent stage identification and emission change analysis.

[0063] Based on the type of production event, emissions are identified for cement production process parameters to obtain the emission change results corresponding to the type of production event.

[0064] The method for obtaining emission change results corresponding to production event types is as follows: Based on the identification of production event types, continuous change scanning processing is performed on the flue gas emission data within the time interval of each production event type.

[0065] During the continuous change scanning process, emission change initiation stage identification processing is performed on the flue gas emission data within the sliding analysis window. When the carbon dioxide concentration rises from below the initiation change threshold to above the initiation change threshold, or falls from above the initiation change threshold to below the initiation change threshold, and this change continues within three continuous sliding analysis windows, and the change in any of the emission indicators—carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration, and flue gas particulate matter concentration—exceeds its corresponding change threshold and remains within three continuous sliding analysis windows, the corresponding time interval is determined as the emission change initiation stage corresponding to the production event type.

[0066] Within the sliding analysis window, emission change duration identification processing is performed. When the carbon dioxide concentration is continuously higher than the high value threshold or continuously lower than the low value threshold for three consecutive sliding analysis windows, and at the same time, the values ​​of any emission index among nitrogen oxides, sulfur dioxide, and particulate matter are continuously higher than their respective high value thresholds or continuously lower than their respective low value thresholds for three consecutive sliding analysis windows, and this state is maintained within the three consecutive sliding analysis windows, the corresponding time interval is determined as the emission change duration corresponding to the production event type. The emission change duration reflects the stable period of index change and is used to determine the termination time and duration of the change.

[0067] After identifying the initial stage and duration of emission changes, the numerical changes of each emission indicator are statistically analyzed. The first sampling time of the time interval corresponding to the initial stage of emission change is taken as the start time of emission change, and the end time of the duration of emission change is taken as the end time of emission change. The time length between the start time and the end time of emission change is recorded as the duration of emission change.

[0068] The starting values ​​of each emission index in the flue gas emission data within the time interval corresponding to the initial stage of emission change are used as the baseline values, and the emission index values ​​corresponding to the end time of the emission change duration stage are used as the termination values. The absolute value of the difference between the termination value and the baseline value is recorded as the emission change range of each emission index. The absolute value of the difference in the value of carbon dioxide concentration is recorded as the carbon emission change range, the absolute value of the difference in the value of nitrogen oxide concentration is recorded as the nitrogen oxide emission change range, the absolute value of the difference in the value of sulfur dioxide concentration is recorded as the sulfur dioxide emission change range, and the absolute value of the difference in the value of the value of flue gas particulate matter concentration is recorded as the particulate matter emission change range.

[0069] The changes in emissions include changes in carbon emissions, nitrogen oxide emissions, sulfur dioxide emissions, and particulate matter emissions.

[0070] The types of emission change phases include the initial emission change phase and the sustained emission change phase.

[0071] Through the above processing steps, the emission change results corresponding to each production event type are obtained. The emission change results include the emission change start time, emission change duration, emission change magnitude, and emission change stage type, providing key data for subsequent construction of response maps.

[0072] Based on the emission change results corresponding to the types of production events, a pollution reduction and carbon reduction production response map is constructed.

[0073] The method for constructing a production response map for pollution reduction and carbon reduction is as follows: Based on the emission change results corresponding to each production event type, the start time of emission changes for each emission indicator under the same production event type is sorted and statistically analyzed to obtain the response order of each emission indicator under that production event type. The time difference between the start time of emission changes for each emission indicator and the start time of the production event under the same production event type is statistically analyzed to obtain the response time interval of each emission indicator relative to the production event.

[0074] By structurally associating the response order and response time intervals of various emission indicators under the same production event type, a pollution reduction and carbon reduction production response map is constructed. This map describes the sequential relationship between carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration, and particulate matter concentration under different production event types, as well as the response time intervals of each emission indicator relative to the production event.

[0075] After obtaining the emission change results for each production event type, the start times of emission changes for carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration, and particulate matter concentration under the same production event type are sorted to identify the response order of each emission indicator. An emission indicator response order list and a response time interval list are established according to the production event type, and the response order list and response time interval list are organized into a graph structure. The graph structure uses the production event type as an index to record the response order and response time interval of each emission indicator under that production event type.

[0076] Based on the pollution reduction and carbon reduction production response map, the production process parameters are processed by time offset, and then a synergistic change sequence is constructed. Based on the synergistic change sequence, the pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process.

[0077] The method for time-shifting production process parameters based on the pollution reduction and carbon reduction production response map is as follows: Based on the response order and response time interval of each emission indicator corresponding to the production event type recorded in the pollution reduction and carbon reduction production response map, time-shifting processing is performed on the flue gas emission data. For emission indicators with longer response time intervals, the time series of their flue gas emission data is shifted forward by that response time interval, so that the start time of emission change for that emission indicator is aligned with the start time of emission change for emission indicators with shorter response time intervals.

[0078] By shifting the data of slow-response indicators forward, aligning their starting point of change with that of fast-response indicators, the previously misaligned change curves are brought back to the same time reference, making it possible to statistically correlate changes within a unified window. For example, shifting the carbon dioxide concentration curve forward by a corresponding lag time means that when nitrogen oxides decrease due to control measures, the decrease in carbon dioxide will also be reflected within the same time window, avoiding the misjudgment that pollutants have decreased but carbon has not, thus preventing such misjudgments.

[0079] In one specific embodiment, during the rotary kiln firing stage, statistical analysis of the emission response spectrum for pollution reduction and carbon reduction revealed that the response order of each emission indicator under this type of production event is as follows: nitrogen oxide concentration changes first, followed by carbon dioxide concentration, and the response time intervals between the two are significantly different. Specifically, the spectrum records show that the average response time interval of nitrogen oxide concentration relative to the start time of the production event is 1 minute, while the average response time interval of carbon dioxide concentration is 5 minutes, meaning that the response of carbon dioxide lags behind that of nitrogen oxide by 4 minutes. This will be illustrated using an actual rotary kiln firing stage as an example. Assuming the production event starts at 0 minutes, flue gas emission data is recorded at a sampling frequency of once per minute. In the raw data without time offset processing, the nitrogen oxide concentration decreases significantly from the 1-minute mark, while the carbon dioxide concentration does not begin to show a decreasing trend until the 5-minute mark. For example, the nitrogen oxide concentration at 0 minutes is 300 mg / m³. 3 It dropped to 285 mg / m³ in 1 minute. 3 It then continued to decrease; the carbon dioxide concentration was 800 mg / m³ at 0 minutes. 3 For the first 4 minutes, it remained at 800 mg / m². 3 Around 5 minutes later, it dropped to 780 mg / m³. 3 If a synergy analysis is performed directly on the two within a unified time window, a false impression of "one increasing while the other decreases" will appear within a 1 to 4 minute time period, leading to an erroneous conclusion of negative synergy.

[0080] Based on the response time intervals recorded in the pollution reduction and carbon reduction production response map, time-shifting processing was performed on carbon dioxide data with longer response time intervals. The overall carbon dioxide concentration time series was shifted forward by 4 minutes, meaning the original carbon dioxide concentration value at the 5-minute mark was 780 mg / m³. 3 The values ​​were shifted to the 1-minute mark, the values ​​originally corresponding to the 6-minute mark were shifted to the 2-minute mark, and so on. Simultaneously, the data missing from the first four time points (before 0 minutes) were discarded, or subsequent data were supplemented. After the shift, the starting point of the decrease in carbon dioxide concentration was aligned with that of nitrogen oxide concentration, both beginning to decrease at the 1-minute mark. Observing within a unified time window at this point, both show a synchronous decreasing trend after 1 minute, accurately reflecting the synergistic response relationship between carbon emissions and pollutant emissions after the implementation of control measures. Subsequent synergy analysis can be based on this time-aligned synergistic change sequence, avoiding misjudgments caused by time series misalignment.

[0081] During the time offset processing, the continuity of the offset flue gas emission data is checked within the sliding analysis window. If the change in emission amount of the offset flue gas emission data does not exceed the preset change threshold within three consecutive sliding analysis windows, the time offset processing is marked as effective; otherwise, the time offset processing is marked as invalid and the time offset processing is re-executed.

[0082] By using time offset processing, the emission change stages of carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration corresponding to the same production event type are aligned in time.

[0083] The method for constructing a coordinated change sequence is as follows: After time offset processing, the time-aligned flue gas emission data are arranged in chronological order to construct a coordinated variation sequence. This coordinated variation sequence is based on a unified time axis and records the concentrations of carbon dioxide, nitrogen oxides, sulfur dioxide, and particulate matter at each time point.

[0084] In the coordinated change sequence, the direction of change of each emission indicator within each sliding analysis window is identified. When at least three of the emission indicators, namely carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration, continuously decrease within three consecutive sliding analysis windows and the decrease exceeds their respective preset decrease thresholds, the corresponding time interval is marked as the multi-indicator coordinated decrease stage.

[0085] When carbon dioxide concentration continues to decrease and the decrease exceeds a preset decrease threshold, while two or more emission indicators among nitrogen oxides, sulfur dioxide, and particulate matter concentrations continue to increase and the increase exceeds their respective preset increase thresholds, and this continuous change persists within three consecutive sliding analysis windows, the corresponding time interval is marked as a multi-indicator reverse change phase. Through the above processing, a synergistic change sequence containing the markers for synergistic change phases is obtained. By distinguishing between the two typical change patterns of synergistic decrease and reverse change, the true synergistic relationship between pollutants and carbon emissions can be identified, thereby avoiding analytical errors caused by simple trend judgments.

[0086] The method for performing pollution reduction and carbon reduction synergy analysis based on synergistic change sequences is as follows: Statistical analysis is performed on the multi-indicator synergistic decline phase and the multi-indicator reverse change phase in the synergistic change sequence to calculate the comprehensive synergistic emission reduction degree corresponding to each production event type. The comprehensive synergistic emission reduction degree corresponding to each production event type is used to comprehensively quantify the overall synergistic effect of pollution reduction and carbon reduction under the corresponding production event type.

[0087] In the coordinated change sequence, for each time interval marked as a multi-indicator coordinated decline phase, the emission indicators that actually experienced a continuous decline within that time interval are identified, and the total decline of the emission indicators that actually experienced a continuous decline is calculated. The weighted sum of these total declines is taken as the comprehensive coordinated emission reduction of the multi-indicator coordinated decline phase. For emission indicators that did not experience a continuous decline, their contribution to the coordinated emission reduction of the multi-indicator coordinated decline phase is counted as zero. The comprehensive coordinated emission reduction of the multi-indicator coordinated decline phase is used to comprehensively quantify the net coordinated emission reduction effect generated by the synchronous decline of multiple emission indicators within the corresponding time interval.

[0088] In one specific embodiment, the weights for carbon dioxide concentration are 0.40, nitrogen oxide concentration is 0.30, sulfur dioxide concentration is 0.20, and particulate matter concentration is 0.10. During this time interval, the emission indicators that actually experienced a sustained decrease are carbon dioxide concentration, nitrogen oxide concentration, and sulfur dioxide concentration, with a total decrease in carbon dioxide concentration of 30 mg / m³. 3 The total decrease in nitrogen oxide concentration was 30 mg / m³. 3 The total decrease in sulfur dioxide concentration was 8 mg / m³. 3 Therefore, the comprehensive synergistic emission reduction during the multi-indicator coordinated decline phase is 30×0.4 + 30×0.3 + 8×0.2 + 0×0.1 = 22.6 mg / m³. 3 .

[0089] For each time interval marked as a multi-indicator inverse change phase, the total decrease in carbon dioxide concentration is calculated, and the total increase in nitrogen oxide concentration, sulfur dioxide concentration, and particulate matter concentration is also calculated. The weighted sum of the total increases in each emission indicator, taken as the negative value, is added to the total decrease in carbon dioxide concentration as the comprehensive synergistic emission reduction for the multi-indicator inverse change phase. The comprehensive synergistic emission reduction for the multi-indicator inverse change phase is used to comprehensively quantify the net synergistic effect after the carbon reduction benefits and pollution increase costs offset each other in the complex scenario of carbon emission reduction but some pollutants increase.

[0090] In one specific embodiment, the weights for carbon dioxide concentration are 0.40, nitrogen oxide concentration is 0.30, sulfur dioxide concentration is 0.20, and particulate matter concentration is 0.10. During the time interval of a certain multi-indicator inverse change phase, the total decrease in carbon dioxide concentration is 20 mg / m³. 3 The concentrations of nitrogen oxides, sulfur dioxide, and particulate matter in flue gas all increased, with the total increase in nitrogen oxide concentration being 20 mg / m³. 3 The total increase in sulfur dioxide concentration was 8 mg / m³. 3 The total increase in particulate matter concentration in flue gas was 2 mg / m³. 3Therefore, the weighted decrease in carbon dioxide concentration is first calculated as 20 × 0.40 = 8 mg / m³. 3 Then, the weighted average contribution of each rising indicator is calculated as 20 × 0.30 + 8 × 0.20 + 2 × 0.10 = 7.8 mg / m³. 3 Therefore, the comprehensive synergistic emission reduction during the multi-indicator reverse change phase is 8 - 7.8 = 0.2 mg / m³. 3 .

[0091] The overall synergistic emission reductions for all time intervals under the same production event type are summed to obtain the overall synergistic emission reduction rate for that production event type. By summing and statistically analyzing multiple time intervals under the same production event type, the overall synergistic emission reduction capacity of that production event type throughout the entire production cycle can be obtained, thereby identifying the production stage that contributes the most to synergistic emission reduction.

[0092] In one specific embodiment, if three time intervals are identified under a certain production event type, the comprehensive synergistic emission reduction in the first multi-indicator synergistic reduction phase is 22.6 mg / m³. 3 The comprehensive synergistic emission reduction in the second multi-indicator reverse change phase is 0.2 mg / m³. 3 The comprehensive synergistic emission reduction in the third multi-indicator synergistic reduction phase is 27 mg / m³. 3 Therefore, the overall synergistic emission reduction rate for this type of production event is 22.6 + 0.2 + 27 = 49.8 mg / m³. 3 .

[0093] The comprehensive synergistic emission reduction degree of each production event type is weighted and averaged according to the proportion of its duration to the total production time to obtain the synergistic emission reduction analysis characterization value of the cement production process. The synergistic emission reduction analysis characterization value of the cement production process is used to comprehensively quantify the overall synergistic emission reduction level of the entire cement production process.

[0094] In one specific embodiment, if the duration of this production event type within the statistical period is 180 minutes, and the total production time within the statistical period is 720 minutes, then its time proportion is 180 / 720 = 0.25. Therefore, the contribution of this production event type to the overall coordinated emission reduction is 49.8 × 0.25 = 12.45 mg / m³. 3 By calculating the contribution value of each type of production event and summarizing them, the synergistic emission reduction analysis characterization value of the cement production process is obtained.

[0095] Based on the characterization values ​​of synergistic emission reduction analysis of the cement production process, the results of synergistic emission reduction analysis of the cement production process are obtained.

[0096] The synergistic emission reduction analysis characterization value of the cement production process is compared with the preset synergistic emission reduction analysis characterization threshold. If the synergistic emission reduction analysis characterization value of the cement production process is higher than the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process is marked as a state of good synergistic effect of pollution reduction and carbon reduction. This indicates that under the current production control conditions, carbon dioxide emissions and pollutant emissions can form a synchronous downward trend in the same production stage, and the production process has a high level of synergistic emission reduction.

[0097] If the synergistic emission reduction analysis characterization value of the cement production process is lower than or equal to the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process is marked as insufficient synergistic effect of pollution reduction and carbon reduction. This indicates that there is a situation where carbon emissions and pollutant emissions are not synchronized or change inversely during the current production control process, and the production event control methods in the cement production process need to be optimized to improve the synergistic emission reduction capacity of the production process. Combining the synergistic emission reduction analysis characterization threshold, the synergistic emission reduction level of the production process can be quantitatively determined, thereby providing a clear decision-making basis for production control optimization and improving the synergistic management capacity of pollution reduction and carbon reduction in the cement production process.

[0098] The second aspect of this invention provides a big data-based system for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry, comprising: The event type analysis module is used to collect cement production process parameters, perform parameter fluctuation analysis on the production process parameters, and obtain the production event types.

[0099] The emission identification module is used to identify emissions from cement production process parameters based on the type of production event, and obtain the emission change results corresponding to the type of production event.

[0100] The graph construction module is used to construct pollution reduction and carbon reduction production response graphs based on the emission change results corresponding to the production event types.

[0101] The synergy analysis module is used to perform time offset processing on the production process parameters based on the pollution reduction and carbon reduction production response map, thereby constructing a synergistic change sequence. Based on the synergistic change sequence, pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process.

[0102] It should be noted that the big data-based analysis method and system for the synergistic effect of pollution reduction and carbon reduction in the cement industry also includes a synergistic analysis database, which stores the following thresholds obtained through analysis of historical data: preheating range, low decomposition rate threshold, low concentration threshold, decomposition range, decomposition concentration threshold, low oxygen range, rotation speed range, high load range, high concentration range, cooling range, high air volume range, high flow range, temperature fluctuation threshold, load fluctuation threshold, oxygen content fluctuation threshold, initial change threshold, change threshold, sustained high value threshold, change amplitude threshold, preset decrease amplitude threshold, preset increase amplitude threshold, carbon dioxide concentration weight, nitrogen oxide concentration weight, sulfur dioxide concentration weight, flue gas particulate matter concentration weight, and synergistic emission reduction analysis characterization threshold.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0104] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry based on big data, characterized in that, include: Collect cement production process parameters, perform parameter fluctuation analysis on the production process parameters, and obtain the production event types; Based on the type of production event, emissions are identified for cement production process parameters to obtain the emission change results corresponding to the type of production event. Based on the emission change results corresponding to the types of production events, a pollution reduction and carbon reduction production response map is constructed; Based on the pollution reduction and carbon reduction production response map, the production process parameters are processed by time offset, and then a synergistic change sequence is constructed. Based on the synergistic change sequence, the pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process. The cement production process parameters include kiln operation data, energy consumption data, material operation and reaction parameters, and flue gas emission data. Production event types include the raw material preheating stage, the pre-decomposition stage in the decomposer furnace, the rotary kiln firing stage, the clinker cooling stage, and the kiln system stable operation stage; The emission change start time, emission change duration, emission change magnitude, and emission change stage type are encapsulated into emission change results corresponding to the production event type. The method for constructing a pollution reduction and carbon reduction production response map is as follows: Based on the emission change results corresponding to each production event type, the start time of emission changes of each emission indicator in the flue gas emission data under the same production event type is sorted and statistically analyzed to obtain the response order of each emission indicator under the production event type. The time difference between the start time of emission changes of each emission indicator and the start time of the production event under the same type of production event is statistically analyzed to obtain the response time interval of each emission indicator relative to the production event. By structurally associating the response order and response time interval of various emission indicators under the same production event type, a pollution reduction and carbon reduction production response map is constructed. The pollution reduction and carbon reduction production response map uses production event type as an index to record the response order and response time interval of each emission indicator under that production event type; The method for time-shifting production process parameters based on the pollution reduction and carbon reduction production response map is as follows: Based on the response order and response time interval of each emission indicator corresponding to the production event type recorded in the pollution reduction and carbon reduction production response map, the flue gas emission data is time-shifted to align the emission change stages of carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration corresponding to the same production event type. After aligning the time series of each emission indicator, a coordinated change sequence is constructed based on the corrected flue gas time series data. The changing trend of each emission indicator is determined by a sliding window, and two types of characteristic periods are marked: coordinated decrease and reverse change.

2. The method for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry based on big data as described in claim 1, characterized in that: The method for analyzing parameter fluctuations in the production process is as follows: The kiln operation data, energy consumption data, material operation reaction parameters, and flue gas emission data are arranged sequentially according to a unified time sampling period to construct a continuous time series, and a sliding analysis window is formed in the continuous time series with five sampling periods. By jointly analyzing the various production process parameters within the sliding analysis window, the production event types are obtained.

3. The method for analyzing the synergy of pollution reduction and carbon reduction in the cement industry based on big data as described in claim 1, characterized in that: The method for obtaining the emission change results corresponding to the production event type is as follows: Based on the identification of production event types, continuous change scanning processing is performed on the flue gas emission data within the time interval of each production event type. During the continuous change scanning process, emission change initiation stage identification processing and emission change duration stage identification processing are performed on the flue gas emission data within the sliding analysis window to obtain the emission change stage type. The types of emission change phases include the emission change initiation phase and the emission change duration phase; The start time and duration of emission changes are obtained by analyzing the initial and sustained phases of emission changes. Based on flue gas emission data, the magnitude of emission changes was analyzed.

4. The method for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry based on big data as described in claim 1, characterized in that: The method for constructing the cooperative change sequence is as follows: After completing the time offset processing, the time-aligned flue gas emission data are arranged in chronological order to construct a coordinated change sequence; In the coordinated change sequence, the direction of change of each emission index within each sliding analysis window is identified. When at least three of the emission indices, including carbon dioxide concentration, nitrogen oxide concentration, sulfur dioxide concentration and flue gas particulate matter concentration, continuously decrease within three consecutive sliding analysis windows and the decrease exceeds their respective preset decrease thresholds, the corresponding time interval is marked as the multi-index coordinated decrease stage. When the carbon dioxide concentration continues to decrease and the decrease exceeds the preset decrease threshold, while two or more emission indicators among the nitrogen oxide concentration, sulfur dioxide concentration, and flue gas particulate matter concentration continue to increase and the increase exceeds their respective preset increase thresholds, and remain at this level for three consecutive sliding analysis windows, the corresponding time interval is marked as a multi-indicator reverse change stage, resulting in a coordinated change sequence that includes the coordinated change stage marker.

5. The method for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry based on big data as described in claim 4, characterized in that: The method for performing pollution reduction and carbon reduction synergy analysis based on synergistic change sequences is as follows: Statistical analysis was performed on the multi-indicator coordinated decline phase and the multi-indicator reverse change phase in the coordinated change sequence, and the comprehensive coordinated emission reduction degree corresponding to each production event type was calculated. In the coordinated change sequence, for each time interval marked as a multi-indicator coordinated decline phase, the comprehensive coordinated emission reduction of the multi-indicator coordinated decline phase is identified; For each time interval marked as a multi-indicator inverse change phase, the comprehensive synergistic emission reduction of the multi-indicator inverse change phase is identified; The overall coordinated emission reductions for all time intervals under the same production event type are summed to obtain the overall coordinated emission reduction degree for that production event type. Based on the comprehensive synergistic emission reduction degree of each production event type, the synergistic emission reduction analysis characterization value of the cement production process is obtained.

6. The method for analyzing the synergistic effect of pollution reduction and carbon reduction in the cement industry based on big data as described in claim 5, characterized in that: The method for obtaining the synergistic emission reduction analysis results of the cement production process is as follows: Based on the characterization values ​​of synergistic emission reduction analysis of the cement production process, the synergistic emission reduction analysis results of the cement production process are obtained; The synergistic emission reduction analysis characterization value of the cement production process is compared with the preset synergistic emission reduction analysis characterization threshold. If the synergistic emission reduction analysis characterization value of the cement production process is higher than the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process is marked as having a good synergistic effect of pollution reduction and carbon reduction. If the synergistic emission reduction analysis characterization value of the cement production process is lower than or equal to the preset synergistic emission reduction analysis characterization threshold, the synergistic emission reduction analysis result of the cement production process will be marked as insufficient synergistic effect of pollution reduction and carbon reduction.

7. A big data-based analysis system for the synergistic effect of pollution reduction and carbon reduction in the cement industry, used to implement the method described in any one of claims 1-6, characterized in that, include: The event type analysis module is used to collect cement production process parameters, perform parameter fluctuation analysis on the production process parameters, and obtain the production event types. The emission identification module is used to identify emissions from cement production process parameters based on the type of production event, and obtain the emission change results corresponding to the type of production event. The graph construction module is used to construct pollution reduction and carbon reduction production response graphs based on the emission change results corresponding to the production event types. The synergy analysis module is used to perform time offset processing on the production process parameters based on the pollution reduction and carbon reduction production response map, thereby constructing a synergistic change sequence. Based on the synergistic change sequence, pollution reduction and carbon reduction synergy analysis is performed to obtain the synergistic emission reduction analysis results of the cement production process.

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