A real-time tracing, emission reduction and regulation analysis method for carbon emission of a cement production process

Abstract

The present application relates to the technical field of carbon emission control of cement production, and discloses a carbon emission real-time tracing emission reduction control analysis method for cement production process, which comprises the following steps: assigning discrete identifiers to continuous materials and associating source components; generating discrete residence time probability distribution in combination with equipment parameters and real-time working conditions to construct material space-time mapping index structure; generating heat load index and decoupling total carbon emission rate into basic and strong burning compensation carbon emission components; when carbon emission exceeds the limit, generating a weighted historical working condition data matrix according to the index to extract historical distribution and matching and issuing control instructions; and correcting dispersion coefficients based on clinker test data asynchronously. Through the construction of space-time mapping of continuous probability density and carbon emission decomposition mechanism, the present application solves the space-time dislocation problem caused by nonlinear flow of materials, realizes quantitative mapping of abnormal carbon emission to source factors, and improves the pertinence of emission reduction control and the model accuracy.

Description

Technical Field

[0001] This invention relates to the field of carbon emission control technology in cement production, specifically a method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes. Background Technology

[0002] In cement production, carbon emission monitoring and control are crucial for achieving green manufacturing. Currently, industrial sites typically monitor total carbon emissions in real time by installing gas analyzers on the kiln tail chimney. This is combined with operating parameters such as feed rate, air volume, and coal quantity collected by a distributed control system, using a time-series alignment method to construct a carbon emission analysis model. When anomalies are detected in the end-point carbon emission data, the control system or operators adjust the front-end coal feed rate or raw material feed rate based on the aligned historical data to control carbon emissions within a set range.

[0003] However, cement production processes involve complex thermal equipment such as rotary kilns and homogenization silos, where materials exhibit nonlinear flow, dispersion effects, and multi-silo homogenization effects. Existing technologies largely rely on fixed pure time delays to align front-end material data with end-end carbon emission data. This approach fails to reflect the true residence state of materials within the equipment, leading to spatiotemporal misalignments between end-end carbon emission data and source material properties and historical operating conditions. Furthermore, thermal systems possess thermal inertia, often resulting in compensatory forced combustion operations to address earlier material fluctuations. Current monitoring methods typically treat carbon emissions as a single total indicator, failing to decouple apparent total carbon emissions from the inherent carbon emissions generated by material reactions and compensatory carbon emissions caused by human intervention. When facing carbon emission limits, the system struggles to distinguish whether the anomaly is caused by front-end component fluctuations or operational compensation, thus failing to generate targeted source-end emission reduction and control strategies and easily leading to inappropriate adjustments in the control system.

[0004] On the other hand, during long-term continuous operation, the internal physical state of cement thermal equipment undergoes gradual changes, altering the dispersion characteristics of material flow. Existing analytical models mostly employ statically set physical and time parameters, lacking a feedback verification mechanism based on actual production line output test results. When equipment operating conditions deviate over a long period, the fixed parameter settings can cause data drift. The lack of adaptive correction for key parameters within the traceability model leads to decreased calculation accuracy during long-term operation, making it difficult to guarantee the reliability of emission reduction control. Therefore, this invention provides a real-time traceability and emission reduction control analysis method and system for cement production processes to address the shortcomings of existing technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a real-time carbon emission traceability and control analysis method for cement production processes. This method solves the problem that, due to the nonlinear flow of materials and complex physicochemical reactions in existing cement production processes, there is a spatiotemporal misalignment between end-of-pipe carbon emission data and the properties of upstream source materials and historical operating conditions. This misalignment prevents the system from tracing abnormal carbon emissions back to the specific material unit or specific operating condition fluctuation that caused the abnormality, and consequently, prevents the generation of source-end emission reduction and control strategies when carbon emissions exceed limits.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes, comprising the following steps: Discrete material unit identifiers are assigned to the time-series data stream according to the set cumulative feeding step size. Element analysis data is extracted to construct source-end component feature vectors and associated with the identifiers. The theoretical dwell time is calculated based on equipment structural parameters and operating conditions, and the discrete dwell time probability distribution of process nodes is generated by combining the axial dispersion coefficient. Write the probability distribution of the linear propulsion node into the spatiotemporal mapping index structure; import the identifier of the homogenization node into the homogenization buffer, and perform state update and identifier merging when the trigger condition is met to generate the homogenization discharge unit identifier and write it into the index structure. The total carbon emission rate is calculated, and the current waveform and nitrogen oxide concentration sequence are extracted to generate a heat load index. When a compensatory strong combustion condition is identified, the total carbon emission rate is decoupled from the basic carbon emission component and the strong combustion compensatory carbon emission component. When no condition is identified, the strong combustion compensatory carbon emission component is zero. The carbon emission generation time window is determined by combining the gas detection lag time; when the total carbon emission rate exceeds the limit, the candidate discrete material unit identifier set is extracted according to the time window, the index structure is queried to obtain and recursively synthesize the historical discrete residence time probability distribution; after weighted correction by the residence time correction factor, a weighted historical operating condition matrix is ​​generated by combining the associated source component feature vector and historical operating conditions; control instructions are generated and issued to the distributed control system according to each carbon emission component and the matrix.

[0007] The technical solution of this invention changes the traditional method of data alignment using fixed pure time delay in cement production process modeling. Specifically, addressing the dispersion effect and multi-compartment homogenization effect of material flow in cement thermal equipment, a material spatiotemporal mapping index structure based on continuous time probability density is established by constructing a mechanism for merging discrete residence time probability distribution and homogenization state. Simultaneously, addressing the thermal inertia and operational compensation phenomena in the thermal system, a heat load index is extracted through multi-dimensional time-series features, physically decoupling the apparent carbon emissions at the end of the process into basic carbon emissions generated by inherent material reactions and compensatory carbon emissions generated by additional fuel consumption to compensate for earlier fluctuations. When emission reduction control is triggered, this solution generates a weighted historical operating condition matrix based on a probability model, enabling the issued control commands to distinguish between material composition fluctuations and operational fluctuations. This achieves quantitative mapping and closed-loop control from carbon emission results to front-end influencing factors. The control commands are generated jointly based on the basic carbon emission component, the strong combustion compensation carbon emission component, and the weighted historical operating condition data matrix, and are used for differentiated control of raw material feeding, fuel supply, or ventilation parameters.

[0008] Preferably, the step of extracting elemental analysis data to construct a source-end component feature vector and associating it with the identifier includes: reading elemental analysis data from the device interface of an online X-ray fluorescence spectrometer, extracting the mass fraction parameters of key oxides involved in the silicate thermal calcination reaction to construct a source-end component feature vector; extracting the device feedback time corresponding to the source-end component feature vector, setting a time alignment tolerance window, and aligning the device feedback time with the absolute timestamp of the discrete material unit identifier record; when the timestamp of the discrete material unit identifier does not match a new source-end component feature vector within the time alignment tolerance window, extracting the most recent historical source-end component feature vector on the time axis that meets the preset validity conditions, and writing it into the material unit data record corresponding to the discrete material unit identifier.

[0009] The preset validity conditions include the online X-ray fluorescence spectrometer being in normal working condition, the historical source component feature vector not exceeding the set valid retention time, the key oxide mass fraction being within the preset reasonable range, and not being marked as invalid data by the outlier removal rules.

[0010] Preferably, the step of calculating the theoretical propulsion residence time based on equipment structural parameters and operating conditions, and generating the discrete residence time probability distribution of the process node by combining the axial dispersion coefficient, includes: calculating the theoretical propulsion residence time of discrete material units in the process node based on the rotary kiln geometric parameters and real-time rotation speed; using the theoretical propulsion residence time as the reference time variable, solving the convection-dispersion equation by combining the axial dispersion coefficient to obtain the continuous time probability density of discrete material units in the process node; discretizing the continuous time probability density according to the set control cycle, setting an effective cutoff time window based on the theoretical propulsion residence time, extracting discrete probability sample points within the effective cutoff time window and performing normalization processing to generate the discrete residence time probability distribution.

[0011] Preferably, the step of performing state update and identifier merging includes: assigning a current remaining mass attribute to the discrete material unit identifier imported into the homogenization state data buffer; reading the physical discharge valve status signal and the actual storage capacity data measured by the material level sensor, and generating a state update trigger signal when the physical discharge valve is in the open state and the actual storage capacity data is greater than the effective storage capacity lower limit calibrated according to the homogenization storage design capacity; extracting the discrete material unit identifier in the homogenization state data buffer, calculating the traceability weight of the source discrete material unit retained in the homogenization discharge unit identifier based on the current remaining mass of each discrete material unit; calculating the fusion component feature vector of the homogenization discharge unit identifier based on the source component feature vector and its corresponding traceability weight, and writing the homogenization discharge unit identifier, traceability weight, and fusion component feature vector into the spatiotemporal mapping index structure; and synchronously updating the current remaining mass of each discrete material unit in the homogenization state data buffer based on the principle of proportional discharge.

[0012] Preferably, the step of extracting the current waveform and nitrogen oxide concentration sequence to generate the heat load index includes: extracting the current waveform sequence of the rotary kiln main drive motor and the time series sequence of nitrogen oxide concentration at the kiln head within the current time window; using the rotary kiln main drive motor current waveform sequence and the kiln head nitrogen oxide concentration time series sequence after zero-padding alignment and standardization as input to a dual-channel one-dimensional convolutional neural network; the first channel of the dual-channel one-dimensional convolutional neural network receives the current sequence tensor, and the second channel receives the nitrogen oxide sequence tensor; the feature vectors output by the two channels are flattened and concatenated before being input into a multi-layer fully connected network, and the continuous scalar of the output layer is used as the heat load index.

[0013] Preferably, the step of decoupling the total carbon emission rate from the basic carbon emission component and the forced combustion compensation carbon emission component includes: determining that a sudden increase in raw material feeding has occurred when the growth slope of the raw material feed rate exceeds a set slope threshold, and at least one of the preheater pressure difference, kiln tail negative pressure, or decomposition furnace outlet temperature exceeds a corresponding set limit within a set synchronous judgment time window; determining that a compensatory forced combustion condition has been entered when the heat load index is greater than the normal operating condition benchmark threshold; calculating the forced combustion compensation allocation coefficient based on historical forced combustion condition samples; using the forced combustion compensation allocation coefficient to divide the total carbon emission rate into components; taking the product of the total carbon emission rate and the forced combustion compensation allocation coefficient as the forced combustion compensation carbon emission component; and obtaining the basic carbon emission component by separating the forced combustion compensation carbon emission component from the total carbon emission rate.

[0014] Preferably, the step of weighted correction by residence time correction factor includes: calculating residence time correction factor based on strong burning compensation distribution coefficient and material viscosity sensitivity coefficient; using residence time correction factor to perform time axis compression mapping on the historical discrete residence time probability distribution after recursive synthesis, mapping the probability weights corresponding to timestamps in the original probability distribution to new timestamps; using linear interpolation to realign the offset discrete time points to the control cycle time grid, and performing normalization processing to obtain the corrected discrete residence time probability distribution.

[0015] Preferably, the step of generating control instructions based on each carbon emission component and the matrix and issuing them to the distributed control system includes: weighting and summing the corrected discrete residence time probability distribution corresponding to the candidate discrete material unit with the time series of thermal control parameters at corresponding time points, and generating a weighted historical operating condition data matrix by superimposing them according to the contribution weights to carbon emission events; inputting the basic carbon emission component, the forced combustion compensation carbon emission component, and the weighted historical operating condition data matrix into the multivariate emission reduction control rule library for matching; when the ratio of the basic carbon emission component to the total carbon emission rate exceeds the set dominant proportion threshold and the weighted historical operating condition data matrix shows fluctuations exceeding the set upper or lower limit of the corresponding thermal control parameters, generating control instructions to reduce the raw material feeding rate and increase the preheater induced draft; when the ratio of the forced combustion compensation carbon emission component to the total carbon emission rate exceeds the dominant proportion threshold, generating control instructions to gradually reduce the kiln head coal feed and optimize the primary air ratio.

[0016] Preferably, the method further includes the following steps: obtaining clinker composition analysis data and corresponding analysis timestamps output by the clinker sampling analysis node; subtracting the pre-calibrated sampling and analysis lag time from the analysis timestamps, aligning them to the corresponding clinker kiln exit time window, extracting the aligned clinker chemical composition vector and converting it using the loss on ignition compensation matrix to obtain the converted raw meal composition vector; extracting the clinker free calcium oxide mass fraction, concatenating the clinker free calcium oxide mass fraction with the converted raw meal composition vector to obtain the calcination feature vector; retrieving source-end composition data through a spatiotemporal mapping index structure based on the aligned clinker kiln exit time window, weighting and summing the source-end composition feature vectors according to their contribution weights, and combining them with the target free calcium oxide set value to generate a weighted source-end composition benchmark feature vector; calculating the Mahalanobis distance between the calcination feature vector and the weighted source-end composition benchmark feature vector as a source traceability deviation evaluation index.

[0017] Preferably, the method further includes the following steps: constructing a free calcium oxide deviation penalty term based on the degree of two-sided deviation between the mass fraction of free calcium oxide in clinker and the target free calcium oxide set value; when the free calcium oxide deviation penalty term does not exceed the abnormal sample discrimination threshold, generating sample confidence weights based on the free calcium oxide deviation penalty term, and using the sample confidence weights to weight the traceability deviation evaluation index to construct a cost function; using the numerical perturbation difference method to approximately calculate the partial derivative of the cost function with respect to the axial dispersion coefficient, and using the gradient descent algorithm to asynchronously correct the axial dispersion coefficient.

[0018] The second aspect of the present invention provides a real-time carbon emission traceability, emission reduction, control and analysis system for cement production processes, configured in a data center server or server cluster, and interacting with the field control layer through an industrial communication network and edge computing nodes. The system includes: a data acquisition module, a weight calculation module, an index management module, a traceability retrieval module and a parameter correction module.

[0019] The data acquisition module is used to assign discrete material unit identifiers to the time-series data stream according to the set cumulative feeding step size, extract element analysis data to construct source component feature vectors and associate them with the identifiers; The weight calculation module is used to calculate the theoretical dwell time based on equipment structural parameters and operating conditions, and to generate the discrete dwell time probability distribution of process nodes by combining the axial dispersion coefficient. The index management module is used to build and maintain the spatiotemporal mapping index structure, write the probability distribution of linear propulsion nodes into the spatiotemporal mapping index structure, import the identifier of the homogenization node into the homogenization buffer, perform state update and identifier merging when the trigger condition is met, generate the homogenization discharge unit identifier and write it into the index structure. The source retrieval module is used to calculate the total carbon emission rate, extract current waveforms and nitrogen oxide concentration sequences to generate a heat load index; when a compensatory forced combustion condition is identified, the total carbon emission rate is decoupled from the basic carbon emission component and the forced combustion compensation carbon emission component; when not identified, the forced combustion compensation carbon emission component is zero; the carbon emission generation time window is determined by combining the gas detection lag time; when the total carbon emission rate exceeds the limit, a candidate discrete material unit identifier set is extracted according to the time window, the index structure is queried to obtain and recursively synthesize the historical discrete residence time probability distribution; after weighted correction by the residence time correction factor, a weighted historical operating condition matrix is ​​generated by combining the associated source component feature vectors and historical operating conditions; control commands are generated and issued to the distributed control system based on each carbon emission component and the matrix. The parameter correction module is used to convert and fuse the clinker chemical composition vector to construct a calcination feature vector. The source deviation evaluation index is obtained by calculating the Mahalanobis distance between the calcination feature vector and the weighted source-end component benchmark feature vector. Based on the index, a cost function is constructed to asynchronously correct the axial dispersion coefficient in the weight calculation module.

[0020] This invention provides a method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes. It has the following beneficial effects: 1. This invention generates a discrete residence time probability distribution by combining the physical structure of the equipment with real-time operating parameters, and establishes a homogenization state update and identifier merging mechanism to construct a material spatiotemporal mapping index structure under continuous time probability density. This design changes the traditional method of data alignment relying on fixed lag time, overcomes the spatiotemporal decoupling problem caused by nonlinear material flow and multi-compartment homogenization effect in cement production, and achieves accurate positioning of end-of-pipe carbon emission data to front-end source materials and historical operating conditions.

[0021] 2. This invention generates a heat load index by extracting current waveforms and nitrogen oxide concentration sequences. Upon identifying compensatory forced combustion conditions, it decouples the total carbon emission rate from the basic carbon emission component and the forced combustion compensation carbon emission component. This design can quantitatively distinguish the independent impacts of inherent material composition fluctuations and operational compensation on carbon emissions. Therefore, when carbon emissions exceed limits, it matches a multivariate rule base based on the dominant decoupled component and issues differentiated emission reduction control commands, avoiding the inappropriate adjustment problems easily caused by traditional end-point total quantity control.

[0022] 3. This invention compares the calcination feature vector obtained from clinker sampling and testing nodes with the weighted source-end component benchmark feature vector retrieved in reverse, calculates the Mahalanobis distance as a traceability deviation evaluation index, and constructs a cost function based on this to asynchronously correct the axial dispersion coefficient. This mechanism enables the traceability analysis model to adapt to changes in the internal physical state of the equipment and data drift caused by long-term operation, ensuring the calculation accuracy and control reliability of the spatiotemporal mapping index structure under long-term operating condition fluctuations. Attached Figure Description

[0023] Figure 1 This is an overview diagram of the system architecture of the present invention; Figure 2 The flowchart of the real-time carbon emission traceability, reduction, control and analysis method for cement production process of the present invention is shown below. Figure 3 This is a timing diagram of the source material digital registration and feature construction of the present invention; Figure 4 This is a schematic diagram illustrating the principle of non-ideal flow tracking and probability weight calculation in this invention. Figure 5 This is a schematic diagram illustrating the spatiotemporal index management and state data reconstruction principle of the present invention. Figure 6 This is a flowchart of the carbon emission source analysis decoupling and source tracing control process of the present invention; Figure 7 This is a flowchart of the hysteresis test feedback and abnormal sample suppression parameter correction of the present invention; Figure 8 This is a curve showing the convergence comparison of the regulation of the present invention; Figure 9 This is a continuous statistical distribution diagram of free calcium oxide in clinker according to the present invention. Detailed Implementation

[0024] The technical solutions in 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.

[0025] See attached document Figure 1 This invention provides a real-time carbon emission traceability and emission reduction control analysis system for cement production processes, comprising: a data acquisition module, a weight calculation module, an index management module, a traceability retrieval module, and a parameter correction module.

[0026] A programmable logic controller (PLC) and a distributed control system (DCS) are configured at the field control layer. These control devices perform operational control of the raw material mill, preheater, rotary kiln, and cooler. Field sensors synchronously collect data on the equipment's rotational speed, pressure, temperature, and material flow rates, and acquire gas concentration analysis data from online X-ray fluorescence spectrometers, X-ray diffractometers, and exhaust nodes. The collected data stream is transmitted to edge computing nodes via an industrial communication network. The edge computing nodes perform data preprocessing and protocol conversion, and then push the standardized time-series data stream to the data center server or server cluster.

[0027] The aforementioned data acquisition module, weight calculation module, index management module, traceability retrieval module, and parameter correction module are deployed in a data center server or server cluster. Specifically, the data acquisition module establishes source material data records; the weight calculation module calculates the residence time distribution of materials at each process node; the index management module maintains the mapping relationship between material identifiers, process nodes, time windows, and probability distributions; the traceability retrieval module uses carbon emission anomaly time windows to reverse-search related materials and historical operating conditions; and the parameter correction module corrects the residence time distribution calculation parameters based on clinker testing feedback.

[0028] The data acquisition module continuously receives the time-series data stream forwarded by the edge computing node. At the preset material tracking starting point, the data acquisition module assigns discrete material unit identifiers to the continuous material flow according to the set cumulative feeding step size. The preset material tracking starting point can be the metering point before raw material enters the kiln, the preheater inlet, or other process locations where stable material flow data can be obtained.

[0029] The data acquisition module reads the elemental analysis data output by the online X-ray fluorescence spectrometer, extracts the oxide components to construct the source component feature vector, and establishes a correlation between the source component feature vector and the corresponding discrete material unit identifier.

[0030] The weight calculation module receives the discrete material unit identifiers and characteristic data output by the data acquisition module. Combining the equipment's physical structure parameters and real-time operating condition parameters, the weight calculation module calculates the theoretical propulsion and residence time of the discrete material unit as it flows through downstream process nodes. Using the axial dispersion coefficient, the weight calculation module generates a corresponding discrete residence time probability distribution for each process node based on the residence time distribution function.

[0031] The index management module is responsible for constructing and maintaining the spatiotemporal mapping index structure. For linear advancement nodes, the index management module writes the discrete material unit identifier and its corresponding discrete dwell time probability distribution into the spatiotemporal mapping index structure and stores it persistently. For homogenization nodes, the index management module imports the discrete material unit identifiers entering the homogenization node into the homogenization state data cache.

[0032] The index management module monitors the status signals of the physical discharge valve and the data from the material level sensor. When the set trigger conditions are met, it writes the cached data in the homogenization status data cache into the spatiotemporal mapping index structure and stores it persistently. Within the homogenization status data cache, the index management module performs homogenization status update and identifier merging operations to generate a homogenization discharge unit identifier. It is then written into the spatiotemporal mapping index structure and persisted for storage.

[0033] The source tracing module acquires flue gas flow rate and carbon dioxide concentration data at the kiln tail exhaust node and calculates the real-time total carbon emission rate using the mass balance equation. Simultaneously, the source tracing module extracts the rotary kiln main motor current waveform and the kiln head nitrogen oxide concentration time series to generate a heat load index, which characterizes the thermal load status of the firing zone.

[0034] When the source tracing module identifies a sudden increase in raw material feeding or a compensatory intense combustion condition, it performs carbon emission source analysis and component decoupling. The source tracing module decouples the total carbon emission rate from the basic carbon emission component and the compensated carbon emission component from intense combustion. The basic carbon emission component includes the basic carbon emission contributions from primary decarbonization processes and conventional fuel combustion processes.

[0035] When the real-time total carbon emission rate exceeds the set emission reduction control baseline, the source tracing module calculates the gas detection lag time based on the flue gas duct volume, real-time flue gas flow rate, sampling pipeline parameters, sampling flow rate, and analyzer response time, and corrects the current timestamp of the exhaust node to the carbon emission generation time window.

[0036] The source retrieval module, based on the corrected carbon emission generation time window, queries the spatiotemporal mapping index structure to extract a set of candidate discrete material unit identifiers with a non-zero arrival probability or an arrival probability greater than a set threshold from the decarbonization node of the decomposer, the firing zone node of the rotary kiln, or other preset carbon emission generation nodes within the time window. Among these, the exhaust node provides flue gas detection timestamps and carbon emission rate data, while the preset carbon emission generation nodes determine the actual reaction location or heat contribution location of the discrete material unit.

[0037] The traceability retrieval module further extracts the historical discrete dwell time probability distribution of each candidate discrete material unit at the front-end node and downstream process node, and recursively synthesizes the discrete dwell time probability distribution of each candidate discrete material unit at the process node according to the process topology order of the production line.

[0038] When a candidate discrete material unit passes through a homogenization node, the tracing and retrieval module first uses the homogenization discharge unit identifier. The traceability weight between the source discrete material unit and the source end completes the contribution conversion, and then participates in the probability recursion of the downstream process node.

[0039] The source tracing and retrieval module uses a residence time correction factor to further weight and correct the probability distribution of the historical discrete residence time after recursion and synthesis. Based on the correction result, the source tracing and retrieval module generates a weighted historical operating condition data matrix, and uses the weighted historical operating condition data matrix to match the emission reduction control rule base, generate control commands, and issue them to the distributed control system.

[0040] The parameter correction module obtains the clinker chemical composition vector output by the clinker sampling analysis node, online clinker analysis equipment, or laboratory information system, and aligns the clinker chemical composition vector to the corresponding clinker discharge time window based on the sampling timestamp, sampling and testing lag time, and clinker delivery time.

[0041] The parameter correction module uses a loss-on-ignition compensation matrix to compensate for loss on ignition, converting the clinker chemical composition vector into a converted raw meal composition vector. The module also integrates the mass fraction of free calcium oxide in the clinker obtained from X-ray diffraction or laboratory records to construct a calcination characteristic vector.

[0042] The parameter correction module extracts the weighted source-end component reference feature vector associated with the front end, and calculates the Mahalanobis distance between the calcination feature vector and the weighted source-end component reference feature vector as the traceability deviation evaluation index.

[0043] The parameter correction module constructs a cost function that includes a free calcium oxide deviation penalty term. It uses a gradient descent algorithm to asynchronously correct the axial dispersion coefficient in the weight calculation module. The asynchronous correction result is used to update the discrete dwell time probability distribution calculation in subsequent control cycles.

[0044] See attached document Figure 2 This invention provides a method for real-time carbon emission traceability, reduction, and control analysis in cement production processes. This method can be executed by the aforementioned real-time carbon emission traceability and reduction control analysis system, used to establish spatiotemporal tracking relationships between material units during continuous cement production, and to generate corresponding emission reduction control commands when carbon emission rates are abnormal. The method includes the following steps: S10, access the time-series data stream, allocate discrete material unit identifiers at the preset material tracking starting point according to the set cumulative feeding step length; extract elemental analysis data output by the online X-ray fluorescence spectrometer, construct source-end component feature vectors; and establish association between the source-end component feature vectors and the corresponding discrete material unit identifiers.

[0045] S20, combining the physical structure parameters of the equipment with the real-time operating condition parameters, calculates the theoretical propulsion and residence time of discrete material units flowing through downstream process nodes; using the axial dispersion coefficient and based on the residence time distribution function, generates the corresponding discrete residence time probability distribution for the process nodes.

[0046] S30: For linear propulsion nodes, the discrete material unit identifier and its corresponding discrete residence time probability distribution are written into the spatiotemporal mapping index structure and persistently stored; for homogenization nodes, the discrete material unit identifiers entering the homogenization node are imported into the homogenization state data cache; the physical discharge valve status signal and material level sensor data are monitored, and when the trigger conditions are met, homogenization state update and identifier merging operations are performed to generate homogenization discharge unit identifiers. It is then written into the spatiotemporal mapping index structure and persisted for storage.

[0047] S40: Obtain flue gas flow rate and carbon dioxide concentration data at the kiln tail exhaust node, and calculate the real-time total carbon emission rate; extract the current waveform of the rotary kiln main motor and the time series sequence of nitrogen oxide concentration at the kiln head to generate the heat load index; when identifying the characteristics of sudden increase in raw material feeding or compensatory strong burning, decouple the total carbon emission rate from the basic carbon emission component and the strong burning compensation carbon emission component.

[0048] When the real-time total carbon emission rate exceeds the set emission reduction control baseline, a set of associated candidate discrete material unit identifiers is extracted from the decarbonization node of the decomposer, the firing zone node of the rotary kiln, or other preset carbon emission generation nodes, based on the carbon emission generation time window after gas detection lag correction. The emission reduction control baseline can be a fixed carbon emission rate threshold, a unit product carbon emission threshold calculated based on unit clinker production, or a dynamic carbon emission baseline calculated from historical steady-state operating data.

[0049] The spatiotemporal mapping index structure is queried to extract the historical discrete dwell time probability distribution corresponding to each candidate discrete material unit; the discrete dwell time probability distribution of each process node is recursively synthesized according to the process topology of the production line; the historical discrete dwell time probability distribution after recursion is weighted and corrected again using the dwell time correction factor, and a weighted historical working condition data matrix is ​​generated; control instructions are generated based on the weighted historical working condition data matrix and sent to the distributed control system.

[0050] S50: Obtain the clinker chemical composition vector output by the clinker sampling and analysis node, online clinker analysis equipment, or laboratory information system, and align the clinker chemical composition vector to the corresponding clinker discharge time window according to the sampling timestamp, sampling and testing lag time, and clinker delivery time.

[0051] Loss on ignition compensation matrix is ​​used to compensate for loss on ignition, and the chemical composition vector of clinker is converted into a converted raw meal composition vector. The mass fraction of free calcium oxide in clinker obtained by X-ray diffraction or laboratory records is fused to construct a calcination characteristic vector. The Mahalanobis distance between the calcination characteristic vector and the weighted source-end composition benchmark characteristic vector is calculated as a traceability deviation assessment index.

[0052] A cost function containing a free calcium oxide deviation penalty term is constructed, and the axial dispersion coefficient is asynchronously corrected using a gradient descent algorithm. The asynchronous correction result is used to update the discrete dwell time probability distribution calculation in subsequent control cycles, and does not directly replace the real-time control result of the current control cycle.

[0053] The technical solution of the present invention will be further explained below in conjunction with the specific data sources, calculation methods and parameter update logic of each step.

[0054] See attached document Figure 3 In the method provided by this invention, the discretization registration of source materials and the construction of source component feature vectors step S10 may further include the following sub-steps: S101, the data acquisition module connects to the time-series data stream and assigns discrete material unit identifiers at the preset material tracking starting point according to the set cumulative feeding step length.

[0055] In cement production sites, raw materials typically enter subsequent thermal processing steps as a continuous flow of powdery or granular materials. Directly tracking this continuous material flow point-by-point would result in an excessively large data volume for subsequent spatiotemporal mapping calculations. Therefore, the data acquisition module discretizes the continuous material flow according to a preset cumulative feeding step size.

[0056] Specifically, the distributed control system transmits the instantaneous flow rate parameters of the feeding equipment to the edge computing node via the industrial communication network. After protocol conversion, the edge computing node forwards the corresponding time-series data stream to the data acquisition module. The data acquisition module performs time integration on the received instantaneous flow rate parameters. When the cumulative material mass obtained by integration reaches the set cumulative feeding step size... At that time, the data acquisition module generates the corresponding discrete material unit identifier. .

[0057] In one alternative implementation, the cumulative feeding step length is... The determination is based on the production line's designed capacity per hour and the accuracy of carbon emission traceability, for example, taking 1 ton to 5 tons as a discrete unit. The serial number of the discrete material unit; the identifier of the newly generated discrete material unit. Record the absolute timestamp when the cumulative material feeding step length condition is met.

[0058] S102, the data acquisition module extracts the elemental analysis data output by the online X-ray fluorescence spectrometer and constructs the source component feature vector.

[0059] During the material conveying process, an online X-ray fluorescence spectrometer deployed on-site samples and analyzes materials entering the raw material mill, preheater inlet, or other pre-set material tracking starting points, and outputs elemental analysis data. The data acquisition module reads this elemental analysis data from the online X-ray fluorescence spectrometer's device interface, analyzes and extracts the mass fraction parameters of key oxides involved in the silicate thermal calcination reaction.

[0060] The aforementioned key oxides are used to characterize the chemical composition of the source materials. The data acquisition module constructs a feature vector of source components based on the extracted oxide parameters. The corresponding mathematical expression is: ; In the formula, Indicates the first In the discrete material unit, the first Mass fraction of key oxides; `index` is a positive integer representing the index of the key oxide species. Key oxide species include calcium oxide, silicon dioxide, aluminum oxide, and ferric oxide. Source-end component feature vector. Used to characterize the physicochemical composition of the corresponding discrete material unit when it enters the tracking process.

[0061] S103, the data acquisition module establishes a correlation between the source component feature vector and the discrete material unit identifier.

[0062] The data acquisition module extracts the feature vectors of the source components. The corresponding equipment feedback time. Considering the physical distance between the sampling point of the online X-ray fluorescence spectrometer and the weighing and feeding equipment, the data acquisition module sets a time alignment tolerance window and links the equipment feedback time with the discrete material unit identifier. Align the records with absolute timestamps.

[0063] The time alignment tolerance window is determined based on the material's transit time on the conveying equipment and the analysis delay of the online X-ray fluorescence spectrometer. To avoid the online X-ray fluorescence spectrometer's analysis cycle being longer than the discrete material unit segmentation cycle, resulting in some discrete material units lacking component data, the data acquisition module employs zero-order hold processing during time alignment.

[0064] When a discrete material unit is identified When a new source component feature vector is not matched within the time alignment tolerance window, the data acquisition module extracts the most recent historical source component feature vector that meets preset validity conditions and uses it as the source component feature vector for that discrete material unit. The preset validity conditions include: the online instrument providing elemental analysis data is not in a fault alarm state; the total mass fraction of the extracted key oxides is within a set reasonable mass fraction range; and the time span between the timestamp corresponding to the historical source component feature vector and the current time does not exceed the set maximum failure duration.

[0065] After completing time alignment or zero-order hold assignment, the data acquisition module will transfer the source component feature vectors. Write discrete material unit identifier In the corresponding material unit data records, a binding relationship is formed between the discrete material unit identifier and the source component feature vector. The data acquisition module writes the bound material unit data records into the spatiotemporal mapping index structure and persists them for use in subsequent process node dwell time distribution calculations and carbon emission source retrieval.

[0066] See attached document Figure 4 In the method provided by this invention, step S20, which generates a discrete dwell time probability distribution based on equipment parameters and operating conditions, may further include the following sub-steps: S201, the weight calculation module receives the discrete material unit identifier output by the data acquisition module, and calculates the theoretical propulsion and residence time of the discrete material unit as it flows through the downstream process node by combining the equipment physical structure parameters and real-time operating condition parameters.

[0067] A cement thermal production line includes process nodes such as preheaters, decomposition furnaces, rotary kilns, and coolers. Different process nodes have different structural forms, material delivery methods, and gas-solid contact states; therefore, the residence time of the same discrete material unit varies in different process nodes.

[0068] The weight calculation module pre-reads the fixed geometric parameters of each process node recorded in the equipment ledger and simultaneously obtains the real-time operating condition parameters fed back by the distributed control system. The fixed geometric parameters include the effective internal length, slewing inner diameter, and installation tilt angle of the equipment; the real-time operating condition parameters include the real-time speed of the main drive motor, the system induced draft volume, and operating condition parameters related to the material conveying status.

[0069] For the rotary kiln node, the weight calculation module calculates the first weight based on the rotary kiln's geometric parameters and real-time rotation speed. Discrete material units at nodes Theoretical advancement dwell time within The corresponding mathematical expression is: ; In the formula, Indicates the effective length of the rotary kiln. Indicates the inner diameter of the rotary kiln. Indicates the installation tilt angle of the rotary kiln. This represents the real-time rotational speed of the discrete material unit as it enters the node. Let be the material propulsion resistance constant. The calibration is performed based on the particle size distribution, moisture content, and pre-sintering state of the raw materials entering the kiln, and the value range can be set from 0.015 to 0.035.

[0070] For nodes in suspended or pneumatic conveying processes such as preheater cyclones and decomposition furnaces, the weighting calculation module calculates the equivalent residence time of the material within that node based on the node's effective volume, real-time flue gas flow rate, material circulation load, and gas-solid slip correction coefficient. For nodes in conveyor belts or bucket elevators, the weighting calculation module calculates the corresponding equivalent propulsion time based on the conveying distance and real-time conveying speed.

[0071] S202, the weight calculation module uses the axial dispersion coefficient and the residence time distribution function to calculate the continuous time probability density of discrete material units in the process node.

[0072] In actual production, bulk materials are affected by cylinder friction, particle tumbling, airflow disturbance, and backmixing as they are pushed through process nodes. As a result, the same batch of materials does not arrive at the node outlet all at a single point in time, but rather forms an arrival probability distribution over a certain time range.

[0073] To describe the aforementioned non-ideal flow state, the weight calculation module introduces an axial dispersion coefficient for each process node. Axial dispersion coefficient It is used to characterize the degree of axial mixing of materials at the corresponding process node, which deviates from the ideal propulsion state. Its initial value is set according to the equipment design parameters, historical operating data or on-site calibration results, and is then corrected by the subsequent parameter correction module based on clinker test feedback.

[0074] The weight calculation module advances the dwell time based on theory. Using the reference time variable, combined with the axial dispersion coefficient Solving the convection-dispersion equations yields the discrete material elements at process nodes. Continuous-time probability density probability density The corresponding mathematical expression is: ; In the formula, This indicates the progress time calculated from the moment the discrete material unit enters the process node; Indicates the first A discrete material unit after a time Then reach the process node The probability density at the terminal.

[0075] S203, the weight calculation module samples and normalizes the continuous-time probability density to generate a discrete dwell time probability distribution.

[0076] To adapt to the discrete sampling period and database storage method of the control system, the weight calculation module calculates the weights according to the control period set by the system. For continuous time probability density Discretized sampling is performed. The weight calculation module advances the dwell time based on the theoretical calculation. Set an effective truncation time window and filter out low-probability tail data outside the truncation time window to reduce computational load.

[0077] In one alternative implementation, the effective cutoff time window is set to... The weight calculation module extracts discrete probability sample points within the window and normalizes all sample points. When the sum of the probability values ​​of the sample points approaches zero, the weight calculation module corrects this using a preset minimum protection threshold to avoid division by zero anomalies. After normalization, the sum of the probability values ​​corresponding to each discrete time point is 1.

[0078] Normalized discrete dwell time probability distribution The corresponding mathematical expression is: ; In the formula, To effectively truncate the total number of discrete sampling points within the time window, to Represents a discrete sampling time series; Represents at discrete time points , No. A discrete material unit arrives at the process node. Probability weights at the end.

[0079] Through the above processing, the weight calculation module transforms the continuous arrival process of discrete material units in process nodes into a storable and recursively calculable discrete dwell time probability distribution, providing a computational basis for subsequent spatiotemporal mapping index construction and multi-node carbon emission source tracing retrieval.

[0080] See attached document Figure 5 In the method provided by this invention, step S30 for establishing a spatiotemporal mapping index structure for linear advancement nodes and equalization nodes may further include the following sub-steps: S301, the index management module writes the discrete material unit identifier and the corresponding discrete dwell time probability distribution into the spatiotemporal mapping index structure for linear process nodes that do not experience significant back mixing.

[0081] In the cement production process, the materials in the conveyor belts, bucket elevators, and some discharge chutes are mainly conveyed forward in the order of feeding, with a low degree of axial back-mixing, and can be treated as linear propulsion nodes.

[0082] When the index management module identifies a process node through which the current discrete material unit flows as a linear advancement node, it generates a corresponding mapping record in the spatiotemporal mapping index structure. In one specific implementation, the spatiotemporal mapping index structure is built upon a distributed time-series database.

[0083] The index management module extracts the absolute timestamp and the equipment number of the current linear process node from the discrete material unit identifier as joint index fields. It establishes a correspondence between this discrete material unit identifier and the discrete dwell time probability distribution calculated in step S20, and writes this mapping record into the spatiotemporal mapping index structure and persists it for subsequent cross-process traceability retrieval. The spatiotemporal mapping index structure contains specific field examples in the underlying database. These examples mainly include: the material tracking code as the association primary key, the current equipment station code representing the physical location, the absolute timestamp recording the entry and exit times at process boundaries, an array recording the oxide mass fraction of the chemical composition, a deduced time axis sequence, and the corresponding probability distribution weight array.

[0084] S302, the index management module imports the discrete material unit identifiers that enter the homogenization node into the homogenization status data cache area, and determines whether to trigger the homogenization status update based on the physical discharge valve status signal and the effective storage capacity lower limit.

[0085] In cement production lines, raw material homogenization silos use bottom aeration devices to pneumatically agitate incoming materials, resulting in a near-uniform mixture on a macroscopic scale. For these homogenization nodes, the index management module temporarily stores the identifiers of discrete material units flowing into the homogenization node and their associated data in the homogenization status data cache.

[0086] The homogenization state data buffer is used to record the current remaining mass and source component characteristics of each discrete material unit in the physical homogenization library. The index management module assigns a current remaining mass attribute to each discrete material unit identifier imported into the homogenization state data buffer. The initial value of this attribute is the cumulative feeding step size in step S10. .

[0087] The index management module reads the physical discharge valve status signal and the actual silo capacity data measured by the material level sensor from the field distributed control system via the industrial communication bus. The index management module sets an effective silo capacity lower limit; the effective silo capacity lower limit can be calibrated based on 15% to 20% of the homogenization silo's design capacity to avoid performing homogenization discharge calculations when the silo is empty, at low material levels, or in areas with significant dead material.

[0088] When the physical discharge valve is open and the actual storage capacity is greater than the lower limit of the effective storage capacity, the index management module determines that the current homogenization status update conditions are met and generates a status update trigger signal.

[0089] S303, after receiving the status update trigger signal, the index management module performs traceability weight calculation and identifier merging on the discrete material units in the homogenization status data buffer based on the complete mixing approximation of the homogenization library, and generates a homogenization discharge unit identifier. And write it into the spacetime mapping index structure.

[0090] Under the complete mixing approximation, the material entering the homogenization bin is considered to be uniformly dispersed within the bin after a set homogenization time. The composition of the material currently exiting the bin is obtained by weighting each discrete material unit in the homogenization state data buffer according to its remaining mass.

[0091] Within each discrete calculation cycle that generates a state update trigger signal, the index management module extracts all valid discrete material unit identifiers from the homogenized state data buffer and generates a homogenized discharge unit identifier for the discharge material corresponding to the current discharge cycle. , This indicates the homogenization discharge sequence number.

[0092] The index management module calculates the source-end discrete material units based on the current remaining mass of each discrete material unit. Retained in the homogenization discharge unit identifier Source tracing weights Corresponding The mathematical expression is: ; In the formula, Indicates the time of triggering Discrete material unit at the source The current remaining quality in the homogenized state data buffer; Indicates at time The sum of the current remaining mass of all discrete material units in the homogenized state data buffer.

[0093] Based on this, the index management module will identify the homogenized discharge unit. Discrete material units at each source end Source traceability weight Establish mapping relationships. The index management module further calculates the homogenization discharge unit identifier based on the source component feature vectors of each source discrete material unit and their corresponding traceability weights. The fusion component feature vector enables the homogenization discharge unit identification. Simultaneously record the contribution weight of the source end and the composition information of the fused material.

[0094] The index management module identifies the homogenized discharge unit. Source traceability weight The fused component feature vectors are used as index records for equalization nodes and written into the spatiotemporal mapping index structure.

[0095] Meanwhile, the index management module calculates the total mass of material discharged within the current discharge cycle. Calculate the overall output ratio The calculation method is as follows The index management module manages the overall output ratio. Apply boundary restrictions: when When it is greater than 1, The value is limited to 1; when When it is less than 0, The value is limited to 0 to avoid abnormal calculation results of remaining mass due to flow measurement errors.

[0096] Based on the principle of proportional discharge under the complete mixing approximation, the index management module synchronously updates the current remaining mass of each discrete material unit in the homogenization state data buffer. The update calculation logic is as follows: ; In the formula, Represents discrete material units at the source. Updated remaining mass after the current discharge cycle.

[0097] To control the data size of the homogenized state data buffer, the index management module sets a minimum quality rejection threshold. This minimum quality rejection threshold can be set as the cumulative feeding step size. 1% to 5%. When the remaining mass of a discrete material unit is updated. When the quality falls below the minimum rejection threshold, the index management module removes the discrete material unit identifier and its associated data from the homogenized state data cache.

[0098] After completing the status update of the homogenization node, the index management module combines the linear advancement nodes, dispersion nodes and corresponding mapping relationships before and after the homogenization node according to the production line process topology order, forming the full-process arrival probability distribution from the source discrete material unit to the target process node.

[0099] For two adjacent non-homogenized nodes, the index management module performs a recursive convolution on their discrete dwell time probability distributions. For paths containing homogenized nodes, the index management module first calls the homogenized discharge unit identifier. Discrete material units at the source The source weights between nodes are then combined with the discrete dwell time probability distribution of downstream nodes.

[0100] Through the above processing, the index management module converts the continuous mixing and discharging process in the homogenization tank into a storable and searchable discrete mapping relationship, enabling subsequent source tracing retrieval to simultaneously obtain the source contribution ratio and the downstream node arrival probability.

[0101] See attached document Figure 6 In the method provided by this invention, step S40 of carbon emission source analysis, source tracing, and emission reduction regulation may further include the following sub-steps: S401, the source retrieval module calculates the total carbon emission rate based on the flue gas flow rate and carbon dioxide concentration at the kiln tail exhaust node, and extracts the rotary kiln main motor current waveform and kiln head nitrogen oxide time sequence within the same time window.

[0102] The source tracing module calculates the system's current total carbon emission rate based on the dry basis flue gas volumetric flow rate and carbon dioxide concentration converted to standard conditions, fed back from the continuous emission monitoring system at the kiln tail exhaust node. Meanwhile, the source tracing module determines the source based on the volume of the flue gas flow path. Real-time flue gas volume flow rate Sampling pipeline length Sampling pipeline inner diameter Sampling pump flow rate Or sampling gas flow rate and gas analyzer response time Calculate the gas detection lag time between exhaust gas detection time and carbon emission generation time. .

[0103] In one alternative implementation, the gas detection lag time Calculate using the following formula: ; In the formula, This refers to the gas detection lag time. This refers to the volume of the flue gas flow channel; For real-time flue gas volume flow rate, item This indicates a delay in the transmission of flue gas within the exhaust channel; This refers to the length of the sampling pipeline; For the sampling gas flow rate, item This indicates sampling lag within the sampling pipeline; The response lag time of the gas analyzer.

[0104] Among them, when the system does not directly collect the sampled gas flow rate At that time, based on the sampling pump flow rate and sampling pipeline inner diameter Calculated using the following formula: ; During subsequent source tracing, the source tracing module will use the detection timestamp of the kiln tail exhaust node. Deduct gas detection lag time The actual carbon emission generation time was calculated. : ; Subsequently, the source tracing module uses the calculated carbon emission generation time. Centered on the target, a corresponding carbon emission generation time window is generated according to the set time width.

[0105] During subsequent source tracing, the source tracing module subtracts the gas detection lag time from the detection timestamp of the kiln tail exhaust node to obtain the corresponding carbon emission generation time window. Among these, the total carbon emission rate... The calculation expression is: ; In the formula, This represents the dry basis flue gas volumetric flow rate converted to standard conditions at the current moment. This represents the volume fraction of carbon dioxide. This represents the density of carbon dioxide under standard conditions.

[0106] The source tracing module also extracts the rotary kiln main drive motor current waveform sequence and the kiln head nitrogen oxide concentration time sequence within the current time window based on the system clock. The time window length can be set to 5 to 15 minutes, and the sampling period can be set to 1 second to obtain high-temporal-resolution kiln thermal state data.

[0107] S402, the source retrieval module uses a dual-channel one-dimensional convolutional neural network to process time series and generate a heat load index to characterize the thermal load state of the firing zone.

[0108] In actual production, the temperature distribution in the firing zone and the state of ring formation or material adhesion within the kiln are difficult to measure directly and continuously. However, the load current of the rotary kiln main motor and the concentration of nitrogen oxides at the kiln head fluctuate with changes in material tumbling resistance, combustion intensity, and thermal regime. Based on this correspondence, the source retrieval module uses the current time window sequence and the nitrogen oxide time window sequence as inputs to a dual-channel one-dimensional convolutional neural network. The input data undergoes zero-padding alignment and Z-score normalization before entering the network.

[0109] In terms of network structure, the time window is set to include One sampling point. The value is determined based on the sampling period and the sensitivity of the operating condition identification; for example, when the sampling period is 1 second, The input data dimension can be between 300 and 900. The first channel receives the current sequence tensor, and the second channel receives the nitrogen oxide sequence tensor. The input data dimension of both channels is [missing value]. .

[0110] The first channel sequentially comprises a first one-dimensional convolutional layer, a first max-pooling layer, a second one-dimensional convolutional layer, and a second max-pooling layer. The first one-dimensional convolutional layer has a kernel size of 3 and 16 kernels; the second one-dimensional convolutional layer has a kernel size of 3 and 32 kernels; the max-pooling layer has a pooling window size of 2; and each convolutional layer is followed by a ReLU activation function. The second channel uses the same convolution and pooling structure.

[0111] The feature vectors output from the two channels are flattened and then concatenated. The concatenated feature vector is then input into a multilayer fully connected network. The multilayer fully connected network consists of a first fully connected layer with 64 nodes and a second fully connected layer with 16 nodes. Each fully connected layer is followed by a ReLU activation function. The output layer is a single-neuron structure that outputs a continuous scalar, which serves as the heat load index.

[0112] The dual-channel one-dimensional convolutional neural network was trained offline before the system was put into operation. The training samples were derived from time-series data retained from the historical distributed control system, covering steady-state operating conditions, sudden increase in raw material feeding conditions, compensatory strong burning conditions, and running out of raw material and under-burning conditions.

[0113] The training labels were determined based on the mass fraction of free calcium oxide in the clinker recorded in the laboratory, and normalized according to the correspondence between the mass fraction of free calcium oxide in the clinker and the heat load of the firing zone. Specifically, the target range for the mass fraction of free calcium oxide was set as follows: : When the mass fraction of free calcium oxide in clinker Below At that time, the heat load label The value is 1; When the mass fraction of free calcium oxide in clinker Higher than At that time, the heat load label The value is 0; When the mass fraction of free calcium oxide in clinker lie in When within the specified range, linear normalization mapping is performed according to the following formula: ; In the formula, The mapped heat load label; The upper limit of the target range for the mass fraction of free calcium oxide; The lower limit of the target range for the mass fraction of free calcium oxide; This represents the current mass fraction of free calcium oxide in the clinker.

[0114] Therefore, when the mass fraction of free calcium oxide in the clinker is lower than the target range, it corresponds to a higher heat load label; when the mass fraction of free calcium oxide in the clinker is higher than the target range, it corresponds to a lower heat load label. The model training uses mean squared error as the loss function and employs the Adam optimizer for parameter updates until the validation set loss converges to within the set allowable error threshold.

[0115] After the model training is completed, it enters the production deployment stage. The details of the training and deployment are as follows: the network model that has passed the training is exported into a standard inference format and deployed to the inference engine of the data center server; during the production execution stage, the inference engine locks all model weight parameters and configures independent data processing threads, while hard-constraining the computation latency of a single forward inference to within 50 milliseconds, so as to ensure that the model can respond to the high-frequency time series data of the underlying control system in real time.

[0116] After the model is deployed and put into operation, the source retrieval module extracts the heat load index statistics corresponding to the historical steady-state operation samples, and uses the average value, or the value after adding a set multiple of standard deviation to the average value, as the normal operating condition benchmark threshold for subsequent identification of compensatory strong burning conditions.

[0117] S403, the source retrieval module identifies abnormal operating conditions based on the heat load index and utilizes the forced-fire compensation distribution coefficient. The total carbon emission rate is divided into the basic carbon emission component and the carbon emission component compensated by forced combustion.

[0118] Carbon emissions during cement production primarily originate from the decomposition of carbonates in raw materials and fuel combustion. When the raw material feed rate increases significantly within a short period, production control typically involves increasing the coal feed rate at the kiln head or adjusting combustion conditions to maintain clinker quality. When this type of sudden increase in raw material feed rate is combined with compensatory intense combustion conditions, it increases the contribution of carbon emissions from the fuel side.

[0119] The source retrieval module compares the current heat load index with the normal operating condition baseline threshold. Simultaneously, the source retrieval module extracts time-series data on raw material feed rate, preheater differential pressure, kiln tail negative pressure, and decomposer outlet temperature within the same time window.

[0120] When the growth rate of raw material feed exceeds a set slope threshold, or the cumulative feed deviation per unit time exceeds a set deviation threshold, and at least one of the preheater differential pressure, kiln tail negative pressure, or decomposer outlet temperature exceeds its corresponding set limit within a set synchronization judgment time window, the traceability retrieval module determines that the system has experienced a sudden increase in raw material feed. The set slope threshold, set deviation threshold, and corresponding set limits are all calibrated based on the statistical average of the corresponding operating condition parameters in historical steady-state production cycles, superimposed with a set standard deviation multiple. The set synchronization judgment time window is determined based on the historical average residence time of materials flowing from a preset material tracking starting point to the corresponding process node.

[0121] The slope threshold, deviation threshold, and corresponding limits are all calibrated based on the statistical average of the corresponding operating parameters in historical steady-state production cycles, superimposed with a set standard deviation multiple. The synchronization judgment time window is determined based on the historical average residence time of materials flowing from the preset material tracking starting point to the corresponding process node. When the heat load index is greater than the normal operating condition baseline threshold, the source retrieval module determines that the system has entered a compensatory strong combustion condition. When the raw material feeding surge condition and the compensatory strong combustion condition occur consecutively or overlap within the set time window, the source retrieval module determines that the current carbon emission anomaly includes a compensatory contribution from strong combustion.

[0122] The source tracing and retrieval module calculates the compensation allocation coefficient for severe burns. Compensation distribution coefficient for severe burns The initial calibration data comes from historical strong-burning condition samples, which are automatically extracted from the historical operation database based on the preset strong-burning sample screening logic. This strong-burning sample screening logic requires that the historical data within the candidate time window simultaneously meet the following conditions: the mass fraction of free calcium oxide in clinker is continuously lower than the set lower limit, and the coal feed rate at the kiln head deviates positively from the steady-state average by more than 5%.

[0123] The historical samples of intense firing conditions extracted under this logic include the increase in coal feed at the kiln head, the coal feed at the precalciner, the lower heating value of pulverized coal, the carbon content of pulverized coal, clinker production, the increase in flue gas carbon dioxide concentration, and the corresponding detection results of free calcium oxide in the clinker. The source retrieval module determines the coefficients by fitting historical samples. The initial value is set and updated during operation based on clinker quality feedback. The corresponding mathematical expression is: ; In the formula, This represents the current output heat load index. This is the baseline threshold under normal operating conditions. This is an empirical coefficient, and its value can be set from 0.05 to 0.15. It is used to adjust the effect of the heat load index offset on the forced-fire compensation distribution coefficient. The extent of the impact.

[0124] Using the compensation distribution coefficient of forced burning The source tracing module tracks the total carbon emission rate. Perform component partitioning: As a basic component of carbon emissions, This component serves as a carbon emission compensation component for forced combustion. By classifying this component, the source retrieval module can distinguish between the basic carbon emission contribution caused by changes in raw material carbonate load and the additional carbon emission contribution from the fuel side caused by forced combustion compensation.

[0125] S404 When the carbon emission rate exceeds the set benchmark and triggers the source retrieval, the source retrieval module uses the dwell time correction factor to weight and correct the historical discrete dwell time probability distribution after the production line process topology recursive synthesis.

[0126] Under compensated intense firing conditions, the increased heat load in the firing zone alters the material flow state within the rotary kiln, causing the actual residence time to deviate from the theoretical advance residence time. To minimize the impact of this deviation on the tracing results, the tracing retrieval module uses an intense firing compensation allocation coefficient... Calculate the dwell time correction factor Residence time correction factor The corresponding mathematical expression is: ; In the formula, The viscosity sensitivity coefficient is calibrated based on the rotary kiln type, length-to-diameter ratio, and historical operating data. Its value range can be set from 0.2 to 0.4.

[0127] The source tracing and retrieval module utilizes a dwell time correction factor. The historical discrete dwell time probability distributions retained in the index management module and synthesized through production line process topology recursion are subjected to time axis compression mapping. For the original probability distribution corresponding to the timestamp... The probability weights are then mapped to new timestamps by the source tracing module. Place.

[0128] After completing the time mapping, the source retrieval module uses linear interpolation to realign the offset discrete time points to the system's standard control cycle time grid and performs normalization processing to make the sum of the probability values ​​at each discrete time point equal to 1, thereby obtaining the corrected discrete dwell time probability distribution.

[0129] S405, the source tracing and retrieval module uses the corrected discrete residence time probability distribution to generate a weighted historical operating condition data matrix, and matches it with the multivariate emission reduction and control rule base to generate on-site emission reduction and control instructions.

[0130] Based on the target time point that triggers the tracing, the source retrieval module extracts the time series of thermal control parameters within the historical retrieval time range from the time series database. The thermal control parameters include the coal feed rate of the decomposer, the induced draft rate of the system, and the raw material feed rate.

[0131] The source tracing module performs a weighted summation of the corrected discrete residence time probability distribution for each candidate discrete material unit with the time series of thermal control parameters at corresponding time points. Simultaneously, the source tracing module normalizes the arrival probability of each candidate discrete material unit within the carbon emission generation time window, obtaining the contribution weight of each candidate discrete material unit to the target carbon emission event. The source tracing module then superimposes the weighted results for each candidate discrete material unit according to its contribution weight, generating a weighted historical operating condition data matrix characterizing the heating process of the materials associated with the target carbon emission event.

[0132] Subsequently, the source tracing and retrieval module takes the basic carbon emission component, the forced combustion compensation carbon emission component, and the weighted historical operating condition data matrix as inputs, and performs rule matching in the pre-set multivariate emission reduction and control rule base.

[0133] The entire rule matching process relies on a clearly defined rule base execution strategy; this rule base execution strategy employs a multi-match arbitration mechanism based on priority weights. When real-time input data simultaneously meets the triggering conditions of multiple rules, the execution engine prioritizes the control instructions with the preset high priority weights; and immediately after the instruction for a specific control object is issued, it activates the anti-jitter timer dead zone, intercepting repeated adjustment instructions in the same direction for the same object within the dead zone time, thereby preventing frequent oscillations in the on-site execution mechanism.

[0134] Specifically, when the ratio of the basic carbon emission component to the total carbon emission rate exceeds the set dominant proportion threshold, such as 60% to 75%, and the weighted historical operating condition data matrix shows that the historical feed rate, raw material carbonate load, or calcium oxide-related indicators in the source component feature vector have exceeded the limit fluctuation, the source tracing and retrieval module determines that the emission reduction focus is on the raw material side and outputs control instructions to reduce the raw material feed rate and increase the preheater induced draft.

[0135] When the ratio of carbon emissions from forced combustion to the total carbon emission rate exceeds the dominant proportion threshold, the source tracing module determines that the focus of emission reduction is on the fuel side and outputs control instructions to gradually reduce the amount of coal fed into the kiln head and optimize the primary air ratio.

[0136] When the weighted historical operating condition data matrix shows that the system induced draft volume, preheater pressure difference, or kiln tail negative pressure has a continuous deviation, and neither the basic carbon emission component nor the forced combustion compensation carbon emission component has reached the single dominant proportion threshold, the source tracing and retrieval module determines that the emission reduction focus is on the ventilation and heat exchange efficiency side, and outputs control instructions to increase the preheater induced draft, optimize the preheater air volume distribution, or stabilize the kiln tail negative pressure, so as to keep the air volume control direction consistent with the abnormal operating conditions of dominant carbon emissions, and avoid issuing induced draft adjustment instructions with opposite control directions to the distributed control system under similar or superimposed fluctuation conditions.

[0137] In one optional implementation, the multivariate emission reduction control rule base includes input fields, judgment condition fields, control object fields, and output instruction fields. The input fields include the proportion of basic carbon emission components, the proportion of carbon emission components compensated by forced combustion, the raw material feed rate, the coal feed rate of the decomposer, the induced draft rate of the system, the preheater pressure difference, the kiln tail negative pressure, and the source component characteristic indicators in the weighted historical operating condition data matrix. The judgment condition fields include the dominant proportion threshold, the upper limit and lower limit of the setting corresponding to each thermal control parameter, and the number of consecutive over-limit cycles. The control object fields include the raw material feeding rate, the coal feed rate of the kiln head, the coal feed rate of the decomposer, the primary air ratio, the induced draft rate of the system, and the preheater air volume allocation. The output instruction fields include the adjustment direction, the adjustment amplitude per cycle, the upper limit of the adjustment rate, and the safety boundary verification result.

[0138] For example, when the proportion of basic carbon emissions exceeds the dominant proportion threshold, and the raw material feed rate or calcium oxide-related indicators in the weighted historical operating condition data matrix exceed the corresponding set limits, the rule base outputs control instructions to reduce the raw material feeding rate and increase the induced draft of the preheater; when the proportion of carbon emissions from forced combustion compensation exceeds the dominant proportion threshold, and the kiln head coal feed rate or heat load index is at a high level, the rule base outputs control instructions to gradually reduce the kiln head coal feed rate and optimize the primary air ratio; when the system induced draft, preheater differential pressure, or kiln tail negative pressure continuously deviates from the set range, and neither of the two types of carbon emissions has reached the dominant proportion threshold, the rule base outputs control instructions to increase the induced draft of the preheater, optimize the preheater air volume distribution, or stabilize the kiln tail negative pressure.

[0139] Before being issued, control commands undergo equipment safety boundary verification, adjustment range limitation, adjustment rate constraint, and continuous control cycle confirmation. Control commands must include at least the target control variable, adjustment direction, single-cycle adjustment range, upper limit of adjustment rate, and safety interlock verification results.

[0140] When the same type of carbon emission anomaly persists for a set number of consecutive control cycles, the source tracing module outputs corresponding secondary control commands to reduce the risk of control oscillations caused by short-term detection noise. These control commands are ultimately sent to the field-level control loop via the industrial communication bus, forming a closed loop for emission reduction control.

[0141] See attached document Figure 7 In the method provided by the present invention, step S50, which involves suppressing abnormal samples and correcting parameters based on delayed laboratory feedback, may further include the following sub-steps: S501, the parameter correction module compensates for the lag time of clinker sampling and testing, and uses the loss on ignition compensation matrix to convert the clinker chemical composition vector into a converted raw meal composition vector.

[0142] At cement production sites, there is typically a 1-2 hour time lag between clinker leaving the rotary kiln and the completion of sampling, sample preparation, and laboratory analysis. The parameter correction module acquires clinker composition analysis data and corresponding analysis timestamps from clinker sampling and analysis nodes, online clinker analysis equipment, or laboratory information systems.

[0143] The parameter correction module subtracts the pre-calibrated sampling and testing lag time from the test timestamp and corrects it by combining the clinker's transport time from the rotary kiln outlet to the sampling point, so that the clinker composition test data is aligned with the corresponding clinker exit time window. The sampling and testing lag time is calibrated based on the average time consumed by on-site sampling, sample preparation, and instrument analysis.

[0144] During the calcination of raw meal into clinker in a rotary kiln, carbonates decompose, releasing carbon dioxide and altering the material's quality. To convert the clinker composition to the raw meal baseline before entering the kiln, the parameter correction module extracts the aligned clinker chemical composition vector and performs the conversion using the loss on ignition compensation matrix. (Clinker chemical composition vector) The corresponding mathematical expression is: ; In the formula, The measured chemical composition vector of clinker includes the mass fractions of calcium oxide, silicon dioxide, aluminum oxide, and ferric oxide. This is the burn-off compensation matrix. In one implementation, Set as a diagonal matrix, with its diagonal elements based on the average loss on burn obtained from historical statistics. Determine and set the value to ; The value can range from 30% to 36%; This is the converted raw material composition vector.

[0145] S502, the parameter correction module combines the mass fraction of free calcium oxide in clinker with the converted raw meal composition vector to construct a calcination feature vector, and calculates the Mahalanobis distance between the calcination feature vector and the weighted source end component benchmark feature vector as a traceability deviation evaluation index.

[0146] The mass fraction of free calcium oxide in clinker is used to reflect the degree of clinker calcination. The parameter correction module extracts the mass fraction of free calcium oxide in clinker obtained from concurrent laboratory tests and concatenates it with the converted raw meal composition vector obtained in step S501 to obtain the calcination feature vector. .

[0147] Based on the aligned clinker discharge time window, the parameter correction module retrieves the corresponding batch material source composition data derived in steps S20 and S30 using a spatiotemporal mapping index structure. The parameter correction module then extracts the set of candidate source discrete material units with non-zero arrival probabilities or arrival probabilities greater than a set threshold within the clinker discharge time window, along with their contribution weights.

[0148] The parameter correction module performs a weighted summation of the corresponding source-end component feature vectors according to the contribution weight of each candidate source-end discrete material unit, resulting in a weighted source-end component vector. The parameter correction module then combines this weighted source-end component vector with the system-defined target free calcium oxide setting to generate a weighted source-end component baseline feature vector. .

[0149] To reduce the impact of differences in the dimensions of different oxide components and variations in normal production on deviation calculations, the parameter correction module uses Mahalanobis distance as the traceability deviation assessment index. The corresponding mathematical expression is: ; In the formula, This is the inverse of the feature covariance matrix calculated statistically based on historical test data. When the number of historical test samples is insufficient, or when the feature covariance matrix is ​​close to singular, the parameter correction module adds a preset regularization term to the feature covariance matrix. Then the inverse is calculated to ensure the numerical stability of the Mahalanobis distance calculation.

[0150] S503, the parameter correction module constructs a free calcium oxide deviation penalty term based on the free calcium oxide deviation of clinker, and uses the gradient descent algorithm to asynchronously correct the axial dispersion coefficient.

[0151] When the rotary kiln is in a state of intense burning with low free calcium oxide, or in a state of under-burning with high free calcium oxide, the clinker test results not only reflect the composition of the raw meal at the source, but are also affected by abnormal thermal conditions. If such samples are directly used for axial dispersion coefficient correction, the parameter updates are likely to deviate from the material residence patterns under normal operating conditions.

[0152] To reduce the impact of abnormal test samples on parameter correction, the parameter correction module constructs a free calcium oxide bias penalty term. The free calcium oxide deviation penalty is determined based on the degree of deviation between the current free calcium oxide mass fraction in the clinker and the target free calcium oxide setpoint. The penalty increases when the free calcium oxide mass fraction in the clinker is significantly lower or significantly higher than the target free calcium oxide setpoint. The corresponding mathematical expression is: ; In the formula, This represents the currently measured mass fraction of free calcium oxide in the clinker; The target free calcium oxide setting value for the system can be set from 1.0% to 1.5%. The penalty weighting coefficient is used to adjust the influence of free calcium oxide bias on the degree of sample participation parameter correction, and its value range can be set from 0.5 to 2.0.

[0153] When free calcium oxide deviation penalty item When the value exceeds the set abnormal sample discrimination threshold, the parameter correction module determines that the current test sample corresponds to a state of excessive heat, underheating, or other abnormal thermal conditions, and skips updating the axial dispersion coefficient corresponding to that sample. The abnormal sample discrimination threshold is based on historical steady-state samples. The statistical distribution is determined, or it is determined based on the allowable deviation range of free calcium oxide set by the process engineers.

[0154] When free calcium oxide deviation penalty item When the abnormal sample discrimination threshold is not exceeded, the parameter correction module adjusts according to... Generate sample credibility weights And utilize sample confidence weights Weighting is applied to the traceability deviation assessment indicators. The larger the value, the higher the sample credibility weight. The smaller the value, the weaker the impact of the corresponding sample on parameter updates. The cost function used for parameter correction. for: ; In the formula, ; The traceability deviation evaluation index is calculated in step S502.

[0155] Because the generation frequency of clinker test data is lower than the sampling frequency of the control system, the parameter correction module triggers asynchronous updates after obtaining discrete test data. The parameter correction module aims to reduce the cost function. With the goal of [target], the axial dispersion coefficient of each process node in the spatiotemporal mapping derivation process is [statistic]. Or a parameter vector composed of axial dispersion coefficients of multiple process nodes. Iterative correction is then performed.

[0156] When the object to be corrected is a parameter vector of multiple process nodes At that time, the parameter correction module applies a disturbance to the axial dispersion coefficient of each node in the actual process path traversed by the candidate discrete material unit. The corresponding numerical gradients are calculated respectively; for process nodes that are not in the candidate path, the axial dispersion coefficient remains unchanged in this asynchronous update. The update calculation logic is as follows: ; In the formula, For asynchronous iteration steps, The learning rate is set. The parameter correction module will update the axial dispersion coefficient. Limited to a preset physical value range Inside; when the calculation result is lower than Time to take When the calculation result is higher Time to take .

[0157] Considering the cost function For axial dispersion coefficient partial derivatives Lacking an explicit analytical expression, this embodiment uses the numerical perturbation difference method for approximate calculation: ; In the formula, The set perturbation step size; This means that while keeping other parameters unchanged, only the first one is changed. The axial dispersion coefficient of each process node increases The cost function value was then recalculated. This represents the cost function value before the perturbation.

[0158] Through the above asynchronous correction process, the parameter correction module updates the axial dispersion coefficient based on the clinker test feedback after eliminating the influence of abnormal test samples, and uses the updated axial dispersion coefficient to calculate the discrete residence time probability distribution of subsequent control cycles.

[0159] During continuous operation of the production line, the data acquisition module sets the material tracking starting point at the conveyor belt before the raw materials enter the warehouse. The distributed control system monitors the material feeding rate in real time. When the cumulative feeding rate reaches 5 tons, the system automatically segments a discrete material unit and assigns it an identifier B101, simultaneously capturing the current source-end composition feature vector output by the online X-ray fluorescence spectrometer. This feature vector includes 42.5% calcium oxide and 13.2% silicon dioxide by mass. The data acquisition module binds this composition feature vector with the discrete material unit identifier B101 and stores it in the time-series database.

[0160] When discrete material unit identifier B101 enters the raw material homogenization silo, the index management module imports its quality attributes into the homogenization status data cache. During subsequent material feeding, the system detects that the physical discharge valve at the bottom of the homogenization silo is open and the material level is within the safe set range, thus triggering a status update. Based on a fully mixed approximation model, the index management module weights and fuses the characteristics of identifier B101 and other concurrently retained discrete material units according to the current remaining mass ratio in the silo, generating a homogenization discharge unit identifier. Simultaneously, based on the discharge ratio, the remaining mass of each discrete material unit at the source end in the buffer area is reduced.

[0161] After leaving the warehouse, the material flow sequentially passes through thermal nodes such as the preheater, decomposition furnace, and rotary kiln. For the rotary kiln node, the weight calculation module obtains the main drive speed of 4 revolutions per minute and the system's induced draft parameters in real time, and calculates the theoretical propulsion time of the material through this node by combining geometric parameters such as the internal effective length in the equipment ledger. The weight calculation module extracts the preset axial dispersion coefficient of the current node and converts the theoretical propulsion time into a discrete residence time probability distribution that varies with time. The probability distribution of each process node is continuously recursively extrapolated according to the process topology, and a dynamic probability tracking path from the material at the origin to the kiln tail exhaust node is constructed in the spatiotemporal mapping index structure.

[0162] During a certain production control cycle, the system monitored a short-term increase in the total carbon emission rate at the kiln tail exhaust node from a steady-state baseline of 180 tons per hour to 215 tons per hour. In response to this change, the source retrieval module subtracted a 45-second physical detection lag time caused by pipeline length and instrument response, calculating the actual carbon emission generation time window. The source retrieval module extracted the rotary kiln main motor current sequence and the kiln head nitrogen oxide time sequence from the same period, inputting them into a dual-channel one-dimensional convolutional neural network for forward calculation. The network output heat load index exceeded the normal baseline threshold, and the source retrieval module determined, based on logical rules, that the current operating condition was superimposed with compensatory strong-fire control. Using the strong-fire compensation allocation coefficient, the source retrieval module decoupled the total carbon emission rate of 215 tons per hour into a base carbon emission component of 190 tons per hour and a strong-fire compensation carbon emission component of 25 tons per hour. The system traced back along the spatiotemporal mapping index structure, determining that the factor causing the increase in the base carbon emission component was an excessive carbonate load of a specific batch of homogenized raw material that passed through the decomposer 40 minutes prior. Based on the decoupling results and the weighted historical operating condition data matrix, the system generates emission reduction control commands to reduce the raw material feeding rate and decrease the system induced draft volume, and sends them to the distributed control system.

[0163] Two hours later, the laboratory information system transmitted the physical analysis results of the clinker from the kiln corresponding to the abnormal time period, showing a free calcium oxide mass fraction of 0.7%. After acquiring this data, the parameter correction module performed loss on ignition compensation calculation and calculated the Mahalanobis distance between the multidimensional calcination feature vector and the weighted source-end component baseline feature vector. Since the free calcium oxide mass fraction deviated from the target optimal range, the parameter correction module determined that the sample was subject to abnormal thermal state disturbance according to preset rules, calculated a distortion penalty term, and reduced the sample's confidence weight accordingly. Subsequently, the parameter correction module constructed a total cost function using the sample confidence weight and the Mahalanobis distance, and asynchronously corrected the axial dispersion coefficient of the spatiotemporal simulation layer using a gradient descent algorithm. This parameter update operation completed the closed-loop self-correction of the underlying physical model parameters without directly interfering with the current on-site control command output.

[0164] Experimental verification and effect comparison: To verify the application effect of this invention, a 30-day continuous operation comparison experiment was conducted based on the operating system of the aforementioned 5000-ton clinker production line. The experiment introduced the traditional fixed-delay source tracing method as a baseline for comparison. The traditional fixed-delay source tracing method sets the residence time of materials in each process as a fixed constant, does not consider axial dispersion probability, does not perform homogenization library mixing calculations, and does not include decoupling from forced combustion compensation for the total carbon emission rate. During the experiment, a total of 35 carbon emission rate exceedance events were recorded, covering three types of operating conditions: raw material fluctuation-dominated, fuel fluctuation-dominated, and typical forced combustion compensation. Specific comparison data are shown in Table 1.

[0165] Table 1: Comparison of Comprehensive Effects under Typical Abnormal Carbon Emission Conditions See Table 1 and Figure 8 Traditional fixed-delay source tracing methods are prone to bias when dealing with thermo-hydraulic coupled conditions such as forced combustion compensation, due to the lack of carbon emission decoupling calculation steps. Their accuracy in carbon emission source analysis for typical forced combustion compensation conditions is below 50%. This invention utilizes a dual-channel one-dimensional convolutional neural network to output a heat load index, decoupling the total carbon emission rate from the basic carbon emission component and the forced combustion compensation carbon emission component. Combined with recursive derivation using discrete residence time probability distribution, the accuracy of carbon emission source analysis for this type of condition is improved to 91.8%. Figure 8 As can be seen from the control convergence comparison curve, the control command output by this system can directly act on the feed setpoint based on the decoupled basic carbon emission component, avoiding the misoperation of fuel ratio by the traditional method, and shortening the average control convergence time of the system after carbon emission exceeds the limit from 48 minutes to 18 minutes.

[0166] See Figure 9To address the 1-2 hour physical time lag in clinker sampling and testing, the parameter correction module of this invention relies on discrete test samples to trigger asynchronous update calculations. By constructing a total cost function that includes a distortion penalty term, the system reduces the computational weight of test samples contaminated by abnormal thermal conditions such as forced combustion or runaway combustion in parameter correction, suppressing the bias guidance of abnormal samples on the axial dispersion coefficient. This anti-contamination asynchronous optimization mechanism ensures that the spatiotemporal mapping index structure maintains the convergence of probability parameters under continuous dynamic operation. Figure 9 The continuous statistical distribution results of free calcium oxide in clinker show that, under this closed-loop control logic, the standard deviation of the fluctuation of free calcium oxide content in clinker leaving the kiln is reduced from 0.38% in the traditional model to 0.15%.

[0167] The solution provided by this invention combines spatiotemporal probability distribution recursion, condition identification based on a one-dimensional convolutional neural network, and an asynchronous closed-loop parameter correction process for pollution prevention. This solution utilizes existing sensor networks and industrial communication architectures to decouple and correlate the total carbon emission rate of the system to specific source materials and process nodes based on time-series state variables and low-frequency laboratory feedback within the production control cycle. This system can automatically output control loop adjustment setpoints for raw material feeding and system induced draft, achieving coordinated control of source-end carbon emission exceedance suppression and end-of-life clinker quality stability. This provides an engineering calculation method for online carbon emission tracking and emission reduction decisions in continuous reaction manufacturing systems.

[0168] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result using substantially the same method falls within the scope of the present invention.

Claims

1. A method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes, characterized in that, Includes the following steps: Discrete material unit identifiers are assigned to the time-series data stream according to the set cumulative feeding step size. Element analysis data is extracted to construct source-end component feature vectors and associated with the identifiers. The theoretical dwell time is calculated based on equipment structural parameters and operating conditions, and the discrete dwell time probability distribution of process nodes is generated by combining the axial dispersion coefficient. Write the probability distribution of the linear propulsion node into the spatiotemporal mapping index structure; import the identifier of the homogenization node into the homogenization buffer, and perform state update and identifier merging when the trigger condition is met to generate the homogenization discharge unit identifier and write it into the index structure. The total carbon emission rate is calculated, and the current waveform and nitrogen oxide concentration sequence are extracted to generate a heat load index. When a compensatory strong combustion condition is identified, the total carbon emission rate is decoupled from the basic carbon emission component and the strong combustion compensation carbon emission component. When not identified, the carbon emission component of forced combustion compensation is zero; The carbon emission generation time window is determined by combining the gas detection lag time; when the total carbon emission rate exceeds the limit, the candidate discrete material unit identifier set is extracted according to the time window, the index structure is queried to obtain and recursively synthesize the historical discrete residence time probability distribution. After weighted correction by the residence time correction factor, a weighted historical operating condition matrix is ​​generated by combining the associated source component feature vectors and historical operating conditions; control commands are generated and issued to the distributed control system based on each carbon emission component and the matrix.

2. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The step of constructing source component feature vectors from extracted elemental analysis data and associating these source component feature vectors with discrete material unit identifiers includes: Elemental analysis data were read from the device interface of an online X-ray fluorescence spectrometer, and the mass fraction parameters of key oxides involved in the thermal calcination reaction of silicates were extracted to construct a source-end component feature vector. Extract the device feedback time corresponding to the feature vector of the source component, set a time alignment tolerance window, and align the device feedback time with the absolute timestamp of the discrete material unit identifier record; When the timestamp of the discrete material unit identifier does not match a new source component feature vector within the time alignment tolerance window, the most recent historical source component feature vector that meets the preset validity conditions on the time axis is extracted and written into the material unit data record corresponding to the discrete material unit identifier.

3. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The steps of calculating the theoretical propulsion dwell time by combining the equipment's physical structure parameters and real-time operating condition parameters, and generating a discrete dwell time probability distribution based on the dwell time distribution function using the axial dispersion coefficient, include: The theoretical propulsion and residence time of discrete material units within process nodes is calculated based on the rotary kiln's geometric parameters and real-time rotation speed. Using the theoretical propulsion residence time as the baseline time variable, and combining the axial dispersion coefficient, the convection-dispersion equation is solved to obtain the continuous time probability density of discrete material units in the process node. The continuous time probability density is discretized according to the set control cycle. An effective cutoff time window is set according to the theoretical propulsion dwell time. Discrete probability sample points within the effective cutoff time window are extracted and normalized to generate a discrete dwell time probability distribution.

4. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The steps of performing the equalization state update and identifier merging operation include: Assign the current remaining mass attribute to the discrete material unit identifier in the imported homogenized state data buffer; Read the status signal of the physical discharge valve and the actual storage capacity data measured by the material level sensor. When the physical discharge valve is in the open state and the actual storage capacity data is greater than the effective storage capacity lower limit calibrated according to the design capacity of the homogenization silo, generate a status update trigger signal. Extract the discrete material unit identifiers from the homogenization state data buffer area, and calculate the traceability weight of the source discrete material unit retained in the homogenization discharge unit identifier based on the current remaining mass of each discrete material unit. Based on the source component feature vector and its corresponding traceability weight, the fusion component feature vector of the homogenized discharge unit identifier is calculated, and the homogenized discharge unit identifier, traceability weight and fusion component feature vector are written into the spatiotemporal mapping index structure; based on the principle of proportional discharge, the current remaining mass of each discrete material unit in the homogenized state data buffer is updated synchronously.

5. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The step of generating a heat load index by extracting the current waveform and the time series sequence of nitrogen oxide concentration includes: Extract the current waveform sequence of the rotary kiln main drive motor and the time sequence sequence of nitrogen oxide concentration at the kiln head within the current time window; The rotary kiln main drive motor current waveform sequence after zero-padding alignment and standardization, and the kiln head nitrogen oxide concentration time series sequence are used as inputs to a dual-channel one-dimensional convolutional neural network. The first channel of the dual-channel one-dimensional convolutional neural network receives the current sequence tensor, and the second channel receives the nitrogen oxide sequence tensor. The feature vectors output by the two channels are flattened and concatenated before being input into a multi-layer fully connected network. The continuous scalar of the output layer is used as the heat load index.

6. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The step of decoupling the total carbon emission rate from the basic carbon emission component and the carbon emission component compensated by forced combustion includes: When the growth rate of raw material feed exceeds the set slope threshold, and at least one of the preheater pressure difference, kiln tail negative pressure or decomposition furnace outlet temperature exceeds the corresponding set limit within the set synchronous judgment time window, it is determined that a sudden increase in raw material feed has occurred. When the heat load index is greater than the normal operating condition reference threshold, it is determined that the condition has entered the compensatory forced combustion condition. The strong-fire compensation allocation coefficient is calculated by combining historical samples of strong-fire conditions. The strong-fire compensation allocation coefficient is used to divide the total carbon emission rate into components. The product of the total carbon emission rate and the strong-fire compensation allocation coefficient is taken as the strong-fire compensation carbon emission component. The basic carbon emission component is obtained by separating the strong-fire compensation carbon emission component from the total carbon emission rate.

7. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The step of weighted correction using the dwell time correction factor includes: The residence time correction factor is calculated based on the forced burning compensation distribution coefficient and the material viscosity sensitivity coefficient. The time axis compression mapping of the historical discrete dwell time probability distribution after recursion synthesis is performed by using the dwell time correction factor, and the probability weights corresponding to the timestamps in the original probability distribution are mapped to the new timestamps. Linear interpolation is used to realign the offset discrete time points to the control cycle time grid, and normalization is performed to obtain the corrected discrete dwell time probability distribution.

8. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The step of generating control commands based on the weighted historical operating condition data matrix and issuing them to the distributed control system includes: The corrected discrete residence time probability distribution corresponding to the candidate discrete material unit is summed with the thermal control parameter time series at corresponding time points, and a weighted historical operating condition data matrix is ​​generated by superimposing the data according to the contribution weight to carbon emission events. The basic carbon emission component, the carbon emission component compensated by forced combustion, and the weighted historical operating condition data matrix are input into the multivariate emission reduction and control rule library for matching. When the ratio of the basic carbon emission component to the total carbon emission rate exceeds the set dominant proportion threshold and the weighted historical operating condition data matrix shows fluctuations exceeding the upper or lower limit of the corresponding thermal control parameter, control commands are generated to reduce the raw material feed rate and increase the induced draft of the preheater. When the ratio of the carbon emission component compensated by forced combustion to the total carbon emission rate exceeds the dominant proportion threshold, control instructions are generated to gradually reduce the coal feed amount at the kiln head and optimize the primary air ratio.

9. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 1, characterized in that, The method further includes the following steps: Obtain the clinker composition analysis data and corresponding analysis timestamps output by the clinker sampling and analysis node; Subtract the pre-calibrated sampling and testing lag time from the test timestamp, align it to the corresponding clinker exit time window, extract the aligned clinker chemical composition vector, and use the loss on ignition compensation matrix to calculate the converted raw meal composition vector. Extract the mass fraction of free calcium oxide from the clinker, and concatenate the mass fraction of free calcium oxide from the clinker with the converted raw meal composition vector to obtain the calcination feature vector; Based on the aligned clinker exit time window, source component data is retrieved through the spatiotemporal mapping index structure. The source component feature vector is weighted and summed according to the contribution weight and combined with the target free calcium oxide set value to generate a weighted source component benchmark feature vector. The Mahalanobis distance between the calcination eigenvector and the weighted source-end component baseline eigenvector is calculated as an indicator for tracing deviation evaluation.

10. The method for real-time source tracing, emission reduction, and control analysis of carbon emissions in cement production processes according to claim 9, characterized in that, The method further includes the following steps: A free calcium oxide deviation penalty term is constructed based on the degree of two-sided deviation between the mass fraction of free calcium oxide in clinker and the target free calcium oxide set value; When the free calcium oxide deviation penalty term does not exceed the abnormal sample discrimination threshold, the sample credibility weight is generated based on the free calcium oxide deviation penalty term, and the cost function is constructed by weighting the source tracing deviation evaluation index using the sample credibility weight. The partial derivative of the cost function with respect to the axial dispersion coefficient is approximated by the numerical perturbation difference method, and the axial dispersion coefficient is asynchronously corrected by the gradient descent algorithm.

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