Integrated treatment method for pyrolysis of organic sludge and roasting of iron tailings

Abstract

The application discloses an integrated treatment method for organic sludge pyrolysis and iron tailing roasting, and relates to the field of sludge treatment.The method comprises the following steps: S10, a processor is used for receiving a multi-node pipe wall space temperature gradient sequence transmitted by a plurality of temperature sensors arranged at a heat exchange interface, and receiving a pressure drop resistance time sequence transmitted by a plurality of pressure sensors arranged at an internal material conveying pipeline; S20, the processor performs a time stamp alignment operation on the multi-node pipe wall space temperature gradient sequence and the pressure drop resistance time sequence, and obtains a synchronous multi-source sensor data set; according to the application, the temperature gradient outside the pipe wall and the internal pressure drop resistance data are collected, the collected data is input into a phase change model after time stamp alignment, a virtual thermal field three-dimensional atlas of the internal blind area of the pipeline is constructed, the specific position can be accurately analyzed, and an instruction is automatically generated to adjust the opening degree of the high-temperature tail gas bypass valve in the corresponding area, so that the front feed cooling and anti-blocking control is realized.

Description

Technical Field

[0001] This application relates to the field of sludge treatment, and in particular to an integrated treatment method for organic sludge pyrolysis and iron tailings roasting. Background Technology

[0002] Traditional sludge landfilling or direct incineration methods not only require large land areas but also easily generate secondary pollution (such as dioxins). In recent years, pyrolysis of organic sludge, utilizing the resulting reducing gases (such as CO and H2) and solid biochar to decouple the reduction of low-grade iron tailings (through magnetized roasting), has become a cutting-edge integrated treatment process that balances "sludge reduction and harmlessness" with "metal resource recovery and purification." However, in actual integrated industrial treatment scenarios, due to the wide range of sludge sources, its moisture content, ash content, and organic matter composition exhibit significant random fluctuations. When the high-temperature waste heat from iron tailings roasting is recycled for sludge pyrolysis, the sludge exhibits extremely strong adhesion and rheological properties in a specific heating zone (usually in the plastic stage). This abrupt change in physical properties easily leads to severe coking, slagging, and even tube clogging at the heat exchange interface (i.e., the furnace tube wall).

[0003] Existing technologies have significant drawbacks in solving this type of coking problem at heat exchange interfaces: Existing furnace monitoring and control systems have significant limitations: limited by single-point sensing and human experience in a "black box" environment, the system cannot intuitively perceive the actual internal thermal field, resulting in a serious lag in early warning; at the same time, due to the lack of dynamic modeling capabilities for complex phase changes and heat conduction topology of materials, traditional algorithms are unable to capture early signs of coking, which can easily lead to blockages and shutdowns; in addition, current control methods fail to achieve a closed-loop synergy between coking prevention and refining yield, and simply relying on cooling to alleviate coking often results in insufficient downstream reduction reactions, ultimately severely sacrificing the recovery grade of metal refining. Summary of the Invention

[0004] This application aims to at least partially solve one of the technical problems in the aforementioned technologies.

[0005] To achieve the above objectives, the present invention provides the following solution: An integrated treatment method for organic sludge pyrolysis and iron tailings roasting, the method being executed by a processor, the method comprising the following steps: Step S10: Receive the multi-node pipe wall space temperature gradient sequence sent by multiple temperature sensors arranged at the heat exchange interface, and receive the pressure drop resistance time sequence sent by multiple pressure sensors arranged in the internal material conveying pipeline.

[0006] Step S20: Perform a timestamp alignment operation on the multi-node pipe wall space temperature gradient sequence and the pressure drop resistance time sequence to obtain a synchronous multi-source sensor dataset.

[0007] Step S30: Input the synchronous multi-source sensor dataset into the preset sludge rheology phase change model to perform unsteady-state heat transfer equation calculation and generate a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure values.

[0008] Step S40: Traverse the real-time temperature and pressure values ​​in the three-dimensional map of the virtual thermal field, and calculate the local wall temperature change rate parameter and pressure drop slope parameter corresponding to the heat exchange interface.

[0009] Step S50: Perform a numerical comparison operation between the local wall temperature change rate parameter and the pressure drop slope parameter and the pre-configured set of plastic coking warning thresholds to generate a multi-dimensional state limit judgment result.

[0010] Step S60: When the multi-dimensional state limit determination result indicates that the local wall temperature change rate parameter or the pressure drop slope parameter is greater than the corresponding threshold in the plastic coking warning threshold set, the target physical location is parsed based on the limit coordinate, and a high-temperature exhaust gas bypass valve opening adjustment command is generated based on the target physical location.

[0011] Preferably, the step S30 of generating a virtual three-dimensional thermal field map containing real-time temperature and real-time pressure values ​​includes: step S31, extracting a set of discrete temperature sampling points and a set of discrete pressure sampling points from the synchronous multi-source sensor dataset.

[0012] Step S32: Input the discrete temperature sampling point set and the discrete pressure sampling point set as initial boundary conditions into the sludge rheological phase change model constructed based on the ordinary differential equation integral evolution algorithm.

[0013] Step S33: In the sludge rheological phase transition model, the initial boundary conditions are solved by integral in the continuous time domain using the ordinary differential equation integral evolution algorithm to calculate the energy evolution state matrix at continuous time steps.

[0014] Step S34: Perform a three-dimensional spatial coordinate mapping projection operation on the energy evolution state matrix to generate a three-dimensional virtual thermodynamic field map containing the real-time temperature value and the real-time pressure value.

[0015] Preferably, the calculation of the local wall temperature change rate parameter and pressure drop slope parameter corresponding to the heat exchange interface in step S40 includes: step S41, constructing a graph structure model based on the spatial coordinates in the three-dimensional map of the virtual thermal field, and assigning the real-time temperature value and the real-time pressure value as node feature vectors to the corresponding nodes in the graph structure model.

[0016] Step S42: Perform graph convolutional network forward propagation calculation on the graph structure model, and perform weighted feature aggregation operation on the node feature vectors of adjacent nodes to obtain the aggregated node feature sequence.

[0017] Step S43: Perform a first-order difference operation on the feature sequence of the aggregated nodes in the time dimension to generate a temperature time difference matrix and a pressure time difference matrix.

[0018] Step S44: Determine the local wall temperature change rate parameter based on the temperature time difference matrix, and determine the pressure drop slope parameter based on the pressure time difference matrix.

[0019] Preferably, the generation of multi-dimensional state limit determination results in step S50 includes step S51, reading the plastic coking warning threshold set from the database, wherein the plastic coking warning threshold set includes a first-level temperature change threshold, a second-level temperature change threshold, a first-level pressure drop threshold, and a second-level pressure drop threshold.

[0020] Step S52: Calculate the difference between the local wall temperature change rate parameter and the first-level temperature change threshold and the second-level temperature change threshold to obtain the first temperature margin value and the second temperature margin value.

[0021] Step S53: Perform a difference calculation operation between the pressure drop slope parameter and the first-level pressure drop threshold and the second-level pressure drop threshold respectively to obtain the first pressure margin value and the second pressure margin value.

[0022] Step S54: Extract the positive and negative sign attributes of the first temperature margin value, the second temperature margin value, the first pressure margin value, and the second pressure margin value, and combine them to generate the multi-dimensional state limit judgment result containing the coking risk level label.

[0023] Preferably, in step S60, the target physical location is parsed based on the over-limit coordinates, and a high-temperature exhaust gas bypass valve opening adjustment command is generated based on the target physical location. This includes step S61, where, when the multi-dimensional state over-limit determination result contains the coking risk level label indicating the highest risk level, the over-limit coordinates that generate the coking risk level label are input to a pre-established three-dimensional equipment space coordinate mapping table.

[0024] Step S62: Query the three-dimensional device space coordinate mapping table, obtain the device area identifier corresponding to the out-of-limit coordinate, and determine the device area identifier as the target physical location.

[0025] Step S63: Determine the target high-temperature exhaust gas bypass valve number data corresponding to the target physical location.

[0026] Step S64: Encapsulate the target high-temperature exhaust gas bypass valve number data and the preset maximum opening value into a data packet in a control communication protocol format, and generate a high-temperature exhaust gas bypass valve opening adjustment command carrying the target high-temperature exhaust gas bypass valve number data.

[0027] Preferably, after generating the high-temperature exhaust gas bypass valve opening adjustment command based on the target physical location, the following processing steps related to metal production or refining are also performed: Step S71, receiving the magnetic measurement sequence data of roasted iron ore sent by the online magnetic susceptibility analyzer arranged at the iron tailings discharge end.

[0028] Step S72: Perform Pearson correlation coefficient calculation on the magnetic measurement sequence data of the roasted iron ore and the synchronous multi-source sensor dataset to obtain the temperature-magnetic coupling correlation matrix.

[0029] Step S73: Extract the feature influence factor vector for the target metal composition based on the temperature-magnetic coupling correlation matrix, and input the feature influence factor vector into the metal production refining yield prediction model to calculate the estimated yield of the next batch of metal refining.

[0030] Step S74: Based on the estimated yield of the next batch of metal refining, generate an iron tailings feed rate correction signal to adjust the total amount of iron tailings feed participating in the metal refining reaction.

[0031] Preferably, obtaining the synchronous multi-source sensor dataset in step S20 includes: step S21, extracting the first series of timestamp data from the multi-node pipe wall space temperature gradient sequence and the second series of timestamp data from the pressure drop resistance time sequence.

[0032] Step S22: Compare the sampling frequencies of the first series of timestamp data and the second series of timestamp data, select the data sequence with the higher sampling frequency as the reference time axis, and construct a unified time alignment grid.

[0033] Step S23: Using the Lagrange polynomial interpolation algorithm, the data points in the data sequence with a lower sampling frequency are mapped to the corresponding nodes in the unified time-aligned grid, and the interpolated supplementary data sequence is calculated.

[0034] Step S24: Perform a matrix concatenation operation on the data containing the interpolated supplementary data sequence and the data corresponding to the reference time axis in the same time domain dimension to generate the synchronous multi-source sensor dataset.

[0035] Preferably, after generating the multi-dimensional state limit determination result, the processor further performs the following update steps.

[0036] Step S81: Receive the actual coking event log file, wherein the actual coking event log file contains the actual timestamp of the coking event and the three-dimensional coordinates of the actual location of the coking event.

[0037] Step S82: Based on the actual occurrence timestamp and the three-dimensional coordinates of the actual occurrence location, extract the corresponding historical local wall temperature change rate and historical pressure drop slope parameters from the database.

[0038] Step S83: Calculate the temperature offset between the historical local wall temperature change rate and the original temperature threshold in the set of plastic coking warning thresholds, and calculate the pressure offset between the historical pressure drop slope parameter and the original pressure threshold in the set of plastic coking warning thresholds.

[0039] Step S84: Using the gradient descent algorithm, perform iterative calculations on the values ​​in the plastic coking warning threshold set based on the temperature offset and the pressure offset to generate an updated plastic coking warning threshold set.

[0040] Preferably, the step S64 of generating the high-temperature exhaust gas bypass valve opening adjustment command carrying the target high-temperature exhaust gas bypass valve number data includes: step S641, obtaining the initial valve opening value corresponding to the target high-temperature exhaust gas bypass valve number data at the current moment.

[0041] Step S642, according to the formula Perform the calculation operation to obtain the target opening increment value data, where, This represents the target opening increment value data. This indicates the temperature difference parameter exceeding the threshold. This indicates the pressure difference parameter exceeding the threshold. and These represent the first adjustment coefficient and the second adjustment coefficient, respectively.

[0042] Step S643: Add the initial valve opening value and the target opening increment value to obtain the target final opening control value parameter.

[0043] Step S644: Encapsulate the target high-temperature exhaust gas bypass valve number data and the target final opening control value parameters into a data packet of a specified control communication protocol format, and generate the high-temperature exhaust gas bypass valve opening adjustment command.

[0044] Preferably, after generating the high-temperature exhaust gas bypass valve opening adjustment command, the processor also performs the following feedback monitoring steps.

[0045] Step S91: The opening adjustment command of the high-temperature exhaust gas bypass valve is sent to the corresponding distributed programmable logic controller via the industrial bus network.

[0046] Step S92: Receive the instruction execution confirmation signal returned by the distributed programmable logic controller after executing the high-temperature exhaust gas bypass valve opening adjustment instruction, as well as the valve real-time position feedback time series data.

[0047] Step S93: Perform a fast Fourier transform on the real-time position feedback time series data of the valve to obtain the valve action spectrum distribution matrix.

[0048] Step S94: Perform pattern matching calculation on the valve action spectrum distribution matrix and the preset standard valve normal operation spectrum template. When the obtained matching similarity value is lower than the preset health benchmark threshold parameter, generate hardware jamming fault alarm information data.

[0049] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: (1): To achieve non-invasive three-dimensional accurate early warning of sludge coking at the heat exchange interface, this invention constructs a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure data by integrating multi-node temperature gradients and pressure drop resistance sequences. Without changing the physical structure of the core furnace body, it accurately captures the precursor characteristics of sludge coking (i.e., abnormal local temperature change rate and pressure drop slope), transforming the traditional post-event shutdown and cleaning into pre-event intelligent avoidance, greatly improving the continuous operation cycle of the system.

[0050] (2): Significantly improves the simulation fidelity of complex phase transition processes in sludge. Based on the integral evolution algorithm of ordinary differential equations, a sludge rheological phase transition model is constructed, and discrete temperature and pressure boundary conditions are solved by integral in the continuous time domain. This scheme accurately characterizes the viscosity abrupt change and energy evolution law of organic sludge during heating, ensuring high time resolution of three-dimensional thermal field mapping, and providing a reliable data foundation for high-precision coking risk assessment.

[0051] (3): The adaptive closed-loop system of "sludge pyrolysis anti-clogging" and "metal refining efficiency enhancement" not only focuses on preventing sludge coking, but also deeply couples thermal parameters with online measured magnetic data of iron tailings, extracts characteristic influencing factors to predict metal refining yield, and dynamically adjusts the iron tailings feed rate accordingly. This mechanism perfectly balances the energy flow of organic sludge pyrolysis and ore roasting and reduction, effectively preventing under-burning or over-burning of ore, and fundamentally improving the refining yield of the target metal.

[0052] (4): To address the issue of false alarms and missed alarms caused by differences in the physical properties of different batches of organic sludge, this system utilizes a gradient descent algorithm to dynamically correct the plastic coking warning threshold by backtracking the temperature and pressure offsets of actual coking events. This data-driven adaptive mechanism reduces reliance on manual adjustments and enables the control system to autonomously iteratively evolve as operating conditions change.

[0053] (5): To address the pain point that bypass valves are prone to mechanical jamming in operating conditions containing volatile substances such as sludge and high-temperature tar, the system performs a Fast Fourier Transform (FFT) on the valve position feedback sequence and identifies minute abnormal friction or vibration in advance through spectral distribution pattern matching. This function can trigger an early warning before the hardware completely seizes up, greatly improving the reliability of the underlying thermal equipment in harsh environments. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is the overall flow chart of the integrated treatment method for organic sludge pyrolysis and iron tailings roasting proposed in this invention; Figure 2 This is a detailed flowchart of the data alignment processing and digital twin graph construction proposed in this invention; Figure 3 This is a detailed flowchart of the multi-dimensional state limit determination and valve control command generation proposed in this invention; Figure 4 This is the flowchart of metal refining yield prediction and feed control feedback proposed in this invention; Figure 5 This is a diagnostic flowchart for the system threshold adaptive update and valve jamming monitoring proposed in this invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] The integrated treatment method of organic sludge pyrolysis and iron tailings roasting according to embodiments of this application will be described below with reference to the accompanying drawings.

[0058] like Figure 1-5 As shown, the present invention provides an integrated treatment method for organic sludge pyrolysis and iron tailings roasting, comprising the following steps: Step S10: The processor receives a multi-node pipe wall space temperature gradient sequence sent by multiple temperature sensors arranged at the heat exchange interface, and receives a pressure drop resistance time sequence sent by multiple pressure sensors arranged in the internal material conveying pipeline.

[0059] In practical applications, this step serves to digitize the physical conditions of the industrial site. The heat exchange interface is a critical physical barrier for heat transfer from the high-temperature roasting gas to the wet sludge. This is because sludge easily becomes sticky and adheres to the inner wall of the pipe when heated to a specific temperature (such as the plastic phase transition zone), leading to blockage (i.e., coking). The multi-node pipe wall spatial temperature gradient sequence is a time-varying temperature set obtained through patch thermocouples, reflecting the thermal field distribution outside the pipe wall; the pressure drop resistance time series is the change in the pipe inlet and outlet pressure difference over time obtained through a differential pressure transmitter, reflecting the smoothness of material flow inside the pipe. This step provides the most basic raw multimodal physical data for subsequent coking prediction.

[0060] For example, in a municipal sludge treatment plant, the system processor receives data once per second from five temperature sensors attached to the outer wall of the heat exchange pipe (such as a multi-node pipe wall space temperature gradient sequence of [380℃, 395℃, 410℃, 405℃, 390℃]), and at the same time receives data from the differential pressure sensors at the beginning and end of the pipe (such as a pressure drop resistance time series with a current resistance of 15 kPa).

[0061] Step S20: The processor performs a timestamp alignment operation on the multi-node pipe wall space temperature gradient sequence and the pressure drop resistance time sequence to obtain a synchronous multi-source sensor dataset.

[0062] In practical applications, this step aims to eliminate data misalignment caused by asynchronous sampling frequencies of heterogeneous sensors. Connecting to the previous step, the multi-node pipe wall space temperature gradient sequence and the pressure drop / resistance time sequence originate from different types of hardware, and their data reporting cycles often differ. Directly using them for joint calculations would lead to incorrect physical state determinations due to time errors. Timestamp alignment ensures a one-to-one correspondence between temperature and pressure data at the same absolute moment. The technical effect is a significant improvement in the accuracy of boundary conditions in subsequent heat transfer model calculations.

[0063] Furthermore, this step can record timestamps, which are the "factory check-in records" that come with each data point.

[0064] To preserve as much high-frequency effective detail as possible, a dense grid must be used as a reference. The uniform time-aligned grid acts like a precise ruler.

[0065] Low-frequency sensors may not collect data at certain high-frequency intervals. Lagrange polynomial interpolation, a classic numerical analysis method, can smoothly and reasonably generate missing values ​​from the preceding and following known values. This improves the resolution of low-frequency data without increasing hardware costs.

[0066] After interpolation and padding, the two sets of data with different lengths are completely identical in terms of the number of rows and time points. At this point, matrix concatenation is performed to "stitch" them together.

[0067] That is, at the same moment, each row now has a pair of precisely corresponding temperature and pressure values, forming a synchronous multi-source sensor dataset.

[0068] For example, the temperature sensor reports 10 times per second (10Hz), while the pressure sensor reports 5 times per second (5Hz). The processor uses a timestamp alignment operation to align them into a synchronous multi-source sensor dataset of 10 times per second, ensuring that the temperature and pressure data within the same millisecond are completely matched.

[0069] The check-in times for extracting temperature data are [12:00:00.100, 12:00:00.200], and the check-in times for extracting pressure data are [12:00:00.150, 12:00:00.350].

[0070] Temperature is 10Hz, pressure is 5Hz, and the system selects 10Hz as the scale on the ruler to establish a unified grid.

[0071] If there is no pressure data at time 12:00:00.200, the system uses the known pressures from the preceding and following times and the Lagrange formula to calculate that the pressure at the missing time should be 15.2 kPa (interpolated to supplement the data sequence).

[0072] Step S30: The processor inputs the synchronous multi-source sensor dataset into the preset sludge rheology phase change model to perform unsteady-state heat transfer equation calculation, generating a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure values.

[0073] In practical applications, referring to the previous step S10, the synchronous multi-source sensor dataset can only represent the "external surface" and "overall macroscopic" state of the pipeline. Coking often occurs at the "internal microscopic" interface of the pipeline, but the interior is filled with high-temperature corrosive materials, making it impossible to directly place sensors. Therefore, this step utilizes the sludge rheological phase change model (a mathematical model pre-constructed based on sludge physical property parameters) combined with the principle of unsteady-state heat transfer to deduce the internal state of the pipe, which cannot be directly measured, from the outside in. The virtual three-dimensional thermal field map is equivalent to establishing a three-dimensional grid inside the system, with each grid point having the calculated real-time temperature and pressure values. This achieves "blind-spot-free, non-invasive" three-dimensional visualization monitoring of the internal thermal state of the system.

[0074] In this step, the time-aligned underlying data can be mathematically split to extract independent temperature vectors (the set of discrete temperature sampling points) and pressure vectors (the set of discrete pressure sampling points) so that they can be used as independent inputs to the calculus equations.

[0075] Since heat transfer and fluid resistance are dynamic processes that change continuously over time, an integral evolution algorithm based on ordinary differential equations (ODEs) is employed. The initial boundary conditions are the currently known external pipe wall environment conditions, which are prerequisites for solving the partial differential equations.

[0076] Connecting to the previous step, since time in the physical world is continuous, the ordinary differential equation integral evolution algorithm iterates continuously with extremely small time steps (such as microseconds) to calculate the energy distribution at the next moment. The resulting energy evolution state matrix is ​​a two-dimensional or multi-dimensional mathematical array containing purely numerical calculation results, representing the accumulation and transfer state of energy within the system.

[0077] Next, through coordinate mapping and projection operations, the values ​​in the energy evolution state matrix are restored to the specific geometric locations of the simulated pipes, ultimately constructing a three-dimensional virtual thermodynamic field map that can be understood by humans or higher-level control logic. The technical achievement is a leap from "one-dimensional time data" to "three-dimensional spatial field".

[0078] For example, the processor extracts five externally measured temperature values ​​from the synchronous multi-source sensor dataset as a discrete temperature sampling point set and three pressure values ​​as a discrete pressure sampling point set.

[0079] The processor will use the 380°C and 15 kPa of the external pipe wall as known initial boundary conditions for solving the heat transfer equation and substitute them into the ODE model.

[0080] The system internally simulated the heat transfer process in the next 0.1 seconds and 0.2 seconds, resulting in a huge numerical matrix (energy evolution state matrix) that records the amount of heat energy transferred in each tiny time slice.

[0081] The processor maps the energy value representing the 800th row of data in the matrix to the coordinate point (X=1.2, Y=0.5, Z=3.0) on the 3D software interface, displaying the real-time temperature value of that point as 425℃.

[0082] Step S40: The processor traverses the real-time temperature value and the real-time pressure value in the three-dimensional map of the virtual thermal field, and calculates the local wall temperature change rate parameter and pressure drop slope parameter corresponding to the heat exchange interface.

[0083] In practical applications, in the previous step S30, it is difficult to accurately determine coking based solely on the absolute real-time temperature value, because coking is a dynamic accumulation process. By differentiating the data in the virtual three-dimensional thermodynamic field (i.e., calculating the rate of change), the local wall temperature change rate parameter (the degree of temperature rise per unit time) can reflect whether heat transfer is hindered due to sludge adhesion to the pipe wall (heat transfer obstruction leads to abnormal local heat accumulation); the pressure drop slope parameter (the value of the increase in resistance per unit time) can reflect whether the cross-sectional area of ​​the pipeline is rapidly decreasing. The technical effect is that it can detect the "early warning signs" of coking in advance.

[0084] Traditional isolated differentiation is susceptible to sensor noise. Graph Convolutional Networks (GCNs) smooth the data of the current node by aggregating information from surrounding neighboring nodes. For example, if a point experiences a sudden drop in temperature while all surrounding points are experiencing a rise, GCN will consider this noise. The final output, the aggregated node feature sequence, is high-fidelity data that has undergone spatial environment "noise filtering" and "comprehensive evaluation".

[0085] First-order difference operations involve subtracting the value from the previous time step from the current value and then dividing by the time interval. The underlying principle can be expressed as: In the formula, The difference value; The feature value at the current time; The feature value at the previous time step; The time interval is defined as follows: Temperature and pressure are processed separately to obtain the temperature time difference matrix and the pressure time difference matrix.

[0086] The processor then remaps the matrix operations at the purely algorithmic level back to the final output parameters with industrial physical significance.

[0087] For example, node A displays a temperature of 425°C, but its neighboring nodes are all around 390°C. After the graph convolutional network performs a weighted average (aggregation) of the neighboring data, the true comprehensive feature of node A is corrected to 400°C (aggregated node feature sequence). The processor subtracts the previous second's 395°C from the current second's aggregated temperature of node A (400°C) to obtain a difference of +5°C. The processor confirms that this difference result represents the physical local wall temperature change rate parameter of +5°C / s.

[0088] Step S50: The processor performs a numerical comparison operation between the local wall temperature change rate parameter and the pressure drop slope parameter and the pre-configured set of plastic coking warning thresholds to generate a multi-dimensional state limit judgment result.

[0089] In practical applications, the set of plastic coking warning thresholds are empirical values ​​pre-stored by the system, representing the limits of safe system operation. By comparing the currently calculated local wall temperature change rate parameter and pressure drop slope parameter with these limit values, the system can automatically determine whether the current operating condition is about to trigger severe coking. The multi-dimensional state limit exceedance determination result is a Boolean value or logical label indicating whether a danger has occurred.

[0090] Industrial alarms cannot be treated with a "one-size-fits-all" approach; they are categorized into different levels of severity. Level 1 typically represents a "minor warning," while Level 2 represents a "serious danger requiring immediate action."

[0091] Connecting to the previous step, by performing subtraction (difference calculation), the first and second temperature margin values ​​reflect how much of a "safe distance" the current state is from danger. If the margin value is negative, it means that the safety line has been crossed.

[0092] By simply determining whether the aforementioned margin value is positive or negative (i.e., the sign attribute), the risk level can be quickly identified. For example, all negative values ​​represent safety, while partially positive values ​​indicate partial exceedance. The aforementioned coking risk level label provides a classification basis for subsequent control strategies.

[0093] For example: The processor reads that the first-level temperature change threshold is 2℃ / min (yellow warning) and the second-level temperature change threshold is 4℃ / min (red alarm). The current parameter is +5℃ / min. Subtracting the first level (2) gives the first temperature margin value of +3, and subtracting the second level (4) gives the second temperature margin value of +1. After calculating the pressure drop slope margin, it is found that the first pressure margin value is -0.1KPa (a negative number means that the warning has not been exceeded), which is in a safe state. Because the temperature margins are all positive and the pressure margins are negative, the system comprehensively judges that the temperature deterioration is serious and generates a coking risk level label of "Level 2 temperature control failure warning".

[0094] Following step S50, the concepts of AI self-learning and closed-loop evolution are introduced. Even the best models and preset thresholds can produce misjudgments. When severe coking does occur on-site, forcing a shutdown for cleaning, the actual fault information is manually recorded; this is the aforementioned actual coking event log file.

[0095] Connecting to the previous step, the system, much like "retrieving surveillance footage," returns to the exact time and location of the accident to see exactly what the rate of change calculated by the system at that time was.

[0096] If the system did not trigger an alarm at that time, it means that the value at that time (e.g., 2.5) did not trigger the original threshold (e.g., 3.0). The temperature offset is the difference between these two values, used to quantify how "sluggish" the system is. The gradient descent algorithm uses this offset as an error gradient to gradually lower the originally set, excessively high safety threshold, allowing the system to learn from its mistakes. The technical effect is to achieve adaptive iterative optimization of the control system's threshold.

[0097] For example, a worker entered a fault record: "Yesterday afternoon at 14:00, a blockage occurred at coordinates (X=2.0, Y=1.0, Z=1.5)." The system reviewed the database at 14:00 yesterday and found that the system's calculated historical local wall temperature change rate at that coordinate was +2.5℃ / min. It was discovered that the originally set threshold was 3.0℃, but coking had already occurred at 2.5℃, with a calculated temperature offset difference of 0.5℃. Using a gradient descent algorithm, the system automatically adjusted the overly lenient alarm threshold of 3.0℃ / min to a more stringent 2.4℃ / min (the updated set of plastic coking warning thresholds) to prevent a recurrence.

[0098] Step S60: When the multi-dimensional state limit determination result indicates that the local wall temperature change rate parameter or the pressure drop slope parameter is greater than the corresponding threshold in the plastic coking warning threshold set, the processor resolves the target physical location based on the limit coordinates and generates a high-temperature exhaust gas bypass valve opening adjustment command based on the target physical location.

[0099] In practical applications, this step is the final step in forming a closed-loop control system. Connecting to the previous step S50, when a high risk is confirmed, the system must take physical intervention. The out-of-limit coordinate is the data point where the anomaly occurs in the 3D map. The processor maps the virtual mathematical coordinates back to the specific physical location of the target in the real workshop, and then generates the opening adjustment command for the high-temperature exhaust gas bypass valve. The purpose of this command is to actively remove a portion of the high-temperature exhaust gas used for heating, thereby precisely reducing the temperature at that local location and allowing the sludge to escape the highly adhesive plastic temperature zone. The technical effect is to achieve unmanned adaptive feedforward control of the equipment to prevent coking, avoiding unplanned downtime for cleaning.

[0100] The 3D equipment spatial coordinate mapping table is a database file similar to a dictionary, recording a one-to-one correspondence between mathematical coordinate systems and actual workshop geographical locations. By looking up the table, the system obtains specific identification numbers that can be recognized by both human engineers and automated control networks—the equipment area identifiers. After identifying the problematic area, it is necessary to find the specific valve that can control the heat in that area. Because the highest risk implies an emergency, the preset maximum opening value (e.g., fully open) is directly used to cool down the area as quickly as possible. This information is packaged into code that industrial networks (such as Modbus or ProfiNet) can understand and then sent out.

[0101] For example, when a "Level 2 Temperature Control Failure Warning" (highest risk) occurs, the processor inputs the coordinates (1.2, 0.5, 3.0) into the mapping table. The system looks up the table and finds that the corresponding equipment area identifier on the factory drawing is "Middle Section of Pyrolysis Kiln No. 2". The system's internal logic determines that to cool down the "Middle Section of Pyrolysis Kiln No. 2", it is necessary and only possible to operate the target high-temperature exhaust gas bypass valve numbered "V-102". The system packages "V-102" and "Opening Degree 100%" into a hexadecimal data packet, generates an instruction, and sends it to the field control cabinet via fiber optic cable.

[0102] Before adjusting the valve, it is essential to know its current state. This step provides a specific proportional-integral (PI) control logic transformation for quantization adjustment. Formula In the formula, and These are constants calibrated based on the inherent characteristics of the thermodynamic equipment hardware. This formula illustrates that the more severe the overheating (…). (larger), or the more violently the resistance increases ( The larger the valve needs to open, the greater the additional angle required. The basic superposition operations determine the final target point of the hardware execution, returning to the general steps of packet transmission in communication protocols.

[0103] For example: When the system prepares to adjust valve V-102, it first reads that it is currently open by 20% (the initial valve opening value). Assume... Excess temperature difference The pressure difference is 2℃ / min. The value is 0.5 kPa / min. Substituting this into the formula... This means that the target opening increment value is calculated to be 15% more. The original 20% is added to the newly calculated 15% increment, resulting in a final target opening control value of 35%. "V-102" and "target opening 35%" are then encapsulated and sent.

[0104] The system must not only prevent blockages but also ensure the quality of the final ironmaking raw materials. The magnetic measurement sequence data of the roasted iron ore reflects how much weakly magnetic hematite was transformed into strongly magnetic magnetite, which is easily refined, after the iron tailings were reduced by pyrolysis gas. Connecting this to the temperature and pressure measurements obtained earlier, this step aims to find the deep mathematical relationship between "operating parameters (temperature / pressure)" and "final product quality (magnetism)". The Pearson correlation coefficient is a statistical algorithm that measures the degree of linear correlation between variables. Its technical effect is to identify which temperature region has the greatest impact on the final ironmaking magnetization. The characteristic influence factor vector is the key temperature node data found in the previous step. Inputting this into a specialized prediction model, the system can predict in advance the output of the next batch of material if the current operating conditions are maintained. If the estimated yield is too low, it indicates insufficient reduction reaction, requiring a reduction in the feed rate to extend the roasting reaction time of the material in the furnace; conversely, the opposite is also true. The technical effect is to ensure the consistent quality of metal refining.

[0105] For example, the online analyzer at the discharge port reported that the magnetization of the currently produced iron tailings powder reached 92%. Calculations showed a Pearson correlation coefficient of 0.85 (highly positive correlation) between the temperature data in the middle section of kiln No. 2 and the final magnetization rate of 92%. Inputting the key temperatures of kiln No. 2 into the model, the system predicted a metal refining yield of 93.5% for the next batch. Due to the extremely high predicted yield of 93.5%, indicating excess reduction capacity, the system generated an acceleration signal, increasing the speed of the iron tailings feed auger motor, feeding an additional 10 tons of material per hour.

[0106] The hardware command path of the control loop. The distributed programmable logic controller (PLC) is the direct "brain" of the field hardware. Once the command is issued, whether the actuator jams is detected. The real-time position feedback time-series data of the valve records the position trajectory of the servo motor rotating and pulling the valve stem inside the valve. Fast Fourier Transform (FFT) can convert the time-domain signal (the trajectory changing over time) into a frequency-domain signal (containing which vibration frequencies). If the valve operates smoothly, its frequency is very simple and stable; if the valve is mechanically jammed due to tar condensation at high temperatures, the motor will exhibit high-frequency vibration and struggle. The valve action spectrum distribution matrix reveals this hidden mechanical anomaly. By comparison, fault detection is achieved. A healthy template has no high-frequency spikes. Through pattern matching calculations, the system can detect the health degradation of the mechanical mechanism under high-temperature and harsh environments before the valve completely jams and becomes unusable. The technical effect is to increase the intelligent device-level fault prediction (PHM) capability of the actuator end, ensuring the extremely high reliability of this system in harsh metal smelting and hazardous waste treatment scenarios.

[0107] For example, an instruction is sent from the main control room via industrial fiber optic cable to the Siemens PLC cabinet located next to kiln No. 2 in the workshop. The PLC replies "Instruction received," and simultaneously, the positioner on the valve begins continuously transmitting the current opening degree: [20%, 22%, 24%,...]. The system performs FFT calculations on the transmitted trajectory data and discovers an abnormal 15Hz sharp high-frequency vibration peak spectrum, in addition to the normal low-frequency drive signal. The system compares the current spectrum with the 15Hz vibration with the healthy template and finds that the similarity is only 60% (below the 85% healthy baseline). Therefore, a pop-up window immediately appears on the monitoring screen: "Valve V-102 is rotating abnormally, posing a risk of tar jamming. Please dispatch a maintenance worker for lubrication and cleaning."

[0108] The present invention has the following advantages: (1): To achieve non-invasive three-dimensional accurate early warning of sludge coking at the heat exchange interface, this invention constructs a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure data by integrating multi-node temperature gradients and pressure drop resistance sequences. Without changing the physical structure of the core furnace body, it accurately captures the precursor characteristics of sludge coking (i.e., abnormal local temperature change rate and pressure drop slope), transforming the traditional post-event shutdown and cleaning into pre-event intelligent avoidance, greatly improving the continuous operation cycle of the system.

[0109] (2): Significantly improves the simulation fidelity of complex phase transition processes in sludge. Based on the integral evolution algorithm of ordinary differential equations, a sludge rheological phase transition model is constructed, and discrete temperature and pressure boundary conditions are solved by integral in the continuous time domain. This scheme accurately characterizes the viscosity abrupt change and energy evolution law of organic sludge during heating, ensuring high time resolution of three-dimensional thermal field mapping, and providing a reliable data foundation for high-precision coking risk assessment.

[0110] (3): The adaptive closed-loop system of "sludge pyrolysis anti-clogging" and "metal refining efficiency enhancement" not only focuses on preventing sludge coking, but also deeply couples thermal parameters with online measured magnetic data of iron tailings, extracts characteristic influencing factors to predict metal refining yield, and dynamically adjusts the iron tailings feed rate accordingly. This mechanism perfectly balances the energy flow of organic sludge pyrolysis and ore roasting and reduction, effectively preventing under-burning or over-burning of ore, and fundamentally improving the refining yield of the target metal.

[0111] (4): To address the issue of false alarms and missed alarms caused by differences in the physical properties of different batches of organic sludge, this system utilizes a gradient descent algorithm to dynamically correct the plastic coking warning threshold by backtracking the temperature and pressure offsets of actual coking events. This data-driven adaptive mechanism reduces reliance on manual adjustments and enables the control system to autonomously iteratively evolve as operating conditions change.

[0112] (5): To address the pain point that bypass valves are prone to mechanical jamming in operating conditions containing volatile substances such as sludge and high-temperature tar, the system performs a Fast Fourier Transform (FFT) on the valve position feedback sequence and identifies minute abnormal friction or vibration in advance through spectral distribution pattern matching. This function can trigger an early warning before the hardware completely seizes up, greatly improving the reliability of the underlying thermal equipment in harsh environments.

[0113] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0114] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0115] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for integrated treatment of organic sludge pyrolysis and iron tailings roasting, characterized in that, The method is executed by a processor and includes the following steps: Step S10: Receive the multi-node pipe wall space temperature gradient sequence sent by multiple temperature sensors arranged at the heat exchange interface, and receive the pressure drop resistance time sequence sent by multiple pressure sensors arranged in the internal material conveying pipeline. Step S20: Perform timestamp alignment on the multi-node pipe wall space temperature gradient sequence and the pressure drop resistance time sequence to obtain a synchronous multi-source sensor dataset; Step S30: Input the synchronous multi-source sensor dataset into the preset sludge rheological phase change model to perform unsteady heat transfer equation calculation and generate a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure values. Step S40: Traverse the real-time temperature value and the real-time pressure value in the three-dimensional map of the virtual thermal field, and calculate the local wall temperature change rate parameter and pressure drop slope parameter corresponding to the heat exchange interface; Step S50: Compare the local wall temperature change rate parameter and the pressure drop slope parameter with the pre-configured set of plastic coking warning thresholds to generate a multi-dimensional state limit judgment result. Step S60: When the multi-dimensional state limit determination result indicates that the local wall temperature change rate parameter or the pressure drop slope parameter is greater than the corresponding threshold in the plastic coking warning threshold set, the target physical location is parsed based on the limit coordinate, and a high-temperature exhaust gas bypass valve opening adjustment command is generated based on the target physical location.

2. The method according to claim 1, characterized in that, Step S30 generates a virtual three-dimensional thermodynamic field map containing real-time temperature and pressure values, including: Step S31: Extract the set of discrete temperature sampling points and the set of discrete pressure sampling points from the synchronous multi-source sensor dataset; Step S32: Input the discrete temperature sampling point set and the discrete pressure sampling point set as initial boundary conditions into the sludge rheological phase change model constructed based on the ordinary differential equation integral evolution algorithm; Step S33: In the sludge rheological phase transition model, the initial boundary conditions are solved by integral in the continuous time domain using the ordinary differential equation integral evolution algorithm to calculate the energy evolution state matrix under continuous time steps. Step S34: Perform a three-dimensional spatial coordinate mapping projection operation on the energy evolution state matrix to generate a three-dimensional virtual thermodynamic field map containing the real-time temperature value and the real-time pressure value.

3. The method according to claim 1, characterized in that, The calculation of the local wall temperature change rate parameter and pressure drop slope parameter corresponding to the heat exchange interface in step S40 includes: Step S41: Construct a graph structure model based on the spatial coordinates in the three-dimensional map of the virtual thermal field, and assign the real-time temperature value and the real-time pressure value as node feature vectors to the corresponding nodes in the graph structure model; Step S42: Perform graph convolutional network forward propagation calculation on the graph structure model, and perform weighted feature aggregation operation on the node feature vectors of adjacent nodes to obtain the aggregated node feature sequence; Step S43: Perform a first-order difference operation on the feature sequence of the aggregated nodes in the time dimension to generate a temperature time difference matrix and a pressure time difference matrix; Step S44: Determine the local wall temperature change rate parameter based on the temperature time difference matrix, and determine the pressure drop slope parameter based on the pressure time difference matrix.

4. The method according to claim 1, characterized in that, The step S50 generates a multi-dimensional state limit violation determination result, including: Step S51: Read the set of plastic coking warning thresholds from the database, wherein the set of plastic coking warning thresholds includes a first-level temperature change threshold, a second-level temperature change threshold, a first-level pressure drop threshold, and a second-level pressure drop threshold. Step S52: Calculate the difference between the local wall temperature change rate parameter and the first-level temperature change threshold and the second-level temperature change threshold to obtain the first temperature margin value and the second temperature margin value. Step S53: Perform a difference calculation operation between the pressure drop slope parameter and the first-level pressure drop threshold and the second-level pressure drop threshold respectively to obtain the first pressure margin value and the second pressure margin value; Step S54: Extract the positive and negative sign attributes of the first temperature margin value, the second temperature margin value, the first pressure margin value, and the second pressure margin value, and combine them to generate the multi-dimensional state limit judgment result containing the coking risk level label.

5. The method according to claim 4, characterized in that, In step S60, the target physical location is resolved based on the out-of-limit coordinates, and a high-temperature exhaust gas bypass valve opening adjustment command is generated based on the target physical location, including: Step S61: When the multi-dimensional state limit exceedance determination result contains the coking risk level label indicating the highest risk level, the limit exceedance coordinates that generate the coking risk level label are input to the pre-established three-dimensional equipment space coordinate mapping table. Step S62: Query the three-dimensional device space coordinate mapping table, obtain the device area identifier corresponding to the over-limit coordinate, and determine the device area identifier as the target physical location; Step S63: Determine the target high-temperature exhaust gas bypass valve number data corresponding to the target physical location; Step S64: Encapsulate the target high-temperature exhaust gas bypass valve number data and the preset maximum opening value into a data packet in a control communication protocol format, and generate a high-temperature exhaust gas bypass valve opening adjustment command carrying the target high-temperature exhaust gas bypass valve number data.

6. The method according to claim 1, characterized in that, After generating the high-temperature exhaust gas bypass valve opening adjustment command based on the target physical location, the following processing steps related to metal production or refining are also performed: Step S71: Receive the magnetic measurement sequence data of roasted iron ore sent by the online magnetic susceptibility analyzer located at the iron tailings discharge end; Step S72: Perform Pearson correlation coefficient calculation on the magnetic measurement sequence data of the roasted iron ore and the synchronous multi-source sensor dataset to obtain the temperature-magnetic coupling correlation matrix; Step S73: Extract the feature influence factor vector for the target metal composition based on the temperature-magnetic coupling correlation matrix, and input the feature influence factor vector into the metal production refining yield prediction model to calculate the estimated yield of the next batch of metal refining. Step S74: Based on the estimated yield of the next batch of metal refining, generate an iron tailings feed rate correction signal to adjust the total amount of iron tailings feed participating in the metal refining reaction.

7. The method according to claim 1, characterized in that, The step S20 of obtaining the synchronized multi-source sensor dataset includes: Step S21: Extract the first series of timestamp data from the multi-node pipe wall space temperature gradient sequence and the second series of timestamp data from the pressure drop resistance time sequence; Step S22: Compare the sampling frequencies of the first series of timestamp data and the second series of timestamp data, select the data sequence with the higher sampling frequency as the reference time axis, and construct a unified time alignment grid; Step S23: Using the Lagrange polynomial interpolation algorithm, the data points in the data sequence with a lower sampling frequency are mapped to the corresponding nodes in the unified time-aligned grid, and the interpolated supplementary data sequence is calculated. Step S24: Perform a matrix concatenation operation on the data containing the interpolated supplementary data sequence and the data corresponding to the reference time axis in the same time domain dimension to generate the synchronous multi-source sensor dataset.

8. The method according to claim 1, characterized in that, After generating the multi-dimensional state limit violation determination result, the processor also performs the following update steps: Step S81: Receive the actual coking event log file, wherein the actual coking event log file includes the actual timestamp of the coking event and the three-dimensional coordinates of the actual location of the coking event. Step S82: Based on the actual occurrence timestamp and the three-dimensional coordinates of the actual occurrence location, extract the corresponding historical local wall temperature change rate and historical pressure drop slope parameters from the database. Step S83: Calculate the temperature offset between the historical local wall temperature change rate and the original temperature threshold in the set of plastic coking warning thresholds, and calculate the pressure offset between the historical pressure drop slope parameter and the original pressure threshold in the set of plastic coking warning thresholds. Step S84: Using the gradient descent algorithm, perform iterative calculations on the values ​​in the plastic coking warning threshold set based on the temperature offset and the pressure offset to generate an updated plastic coking warning threshold set.

9. The method according to claim 5, characterized in that, Step S64, which generates the high-temperature exhaust gas bypass valve opening adjustment command carrying the target high-temperature exhaust gas bypass valve number data, includes: Step S641: Obtain the initial valve opening value corresponding to the target high-temperature exhaust gas bypass valve number data at the current moment; Step S642, according to the formula Perform the calculation operation to obtain the target opening increment value data, where, This represents the target opening increment value data. This indicates the temperature difference parameter exceeding the threshold. This indicates the pressure difference parameter exceeding the threshold. and These represent the first adjustment coefficient and the second adjustment coefficient, respectively. Step S643: Add the initial valve opening value and the target opening increment value to obtain the target final opening control value parameter. Step S644: Encapsulate the target high-temperature exhaust gas bypass valve number data and the target final opening control value parameters into a data packet of a specified control communication protocol format, and generate the high-temperature exhaust gas bypass valve opening adjustment command.

10. The method according to claim 1, characterized in that, After generating the high-temperature exhaust gas bypass valve opening adjustment command, the processor also performs the following feedback monitoring steps: Step S91: The opening adjustment command of the high-temperature exhaust gas bypass valve is sent to the corresponding distributed programmable logic controller via the industrial bus network. Step S92: Receive the instruction execution confirmation signal returned by the distributed programmable logic controller after executing the high-temperature exhaust gas bypass valve opening adjustment instruction, as well as the valve real-time position feedback time series data; Step S93: Perform a fast Fourier transform on the real-time position feedback time series data of the valve to obtain the valve action spectrum distribution matrix; Step S94: Perform pattern matching calculation on the valve action spectrum distribution matrix and the preset standard valve normal operation spectrum template. When the obtained matching similarity value is lower than the preset health benchmark threshold parameter, generate hardware jamming fault alarm information data.

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