Veterinary drug production steam monitoring system and method

Abstract

The application discloses a kind of veterinary drug production steam monitoring system and method, it is related to veterinary drug production steam monitoring technical field, including through the original data set of real-time steam pressure, saturated steam temperature and steam dryness instantaneous value being collected by multiple sensor nodes being deployed in steam pipe network;Pressure and temperature data are input into steam state calculation model, and the steam specific enthalpy value, entropy value and superheat degree numerical value of each pipe section of pipe network are generated;According to the comparison of instantaneous value and historical data of dryness, periodic dryness compensation process is started, and dryness adjustment instruction sequence is generated based on current working condition;Steam energy distribution diagram is constructed using the obtained thermodynamic state parameters, and dynamic optimization is carried out combined with dryness adjustment instruction sequence, and finally steam supply strategy for different production links is generated.The application realizes the global reconstruction of the thermodynamic state of pipe network and the prospective dynamic regulation and control of steam dryness, and improves the accuracy and stability of steam energy utilization in the process of veterinary drug production.

Description

Technical Field

[0001] This invention belongs to the field of steam monitoring technology in veterinary drug production, specifically a steam monitoring system and method for veterinary drug production. Background Technology

[0002] In veterinary drug production, steam is a critical process medium, and its thermodynamic stability directly affects the uniformity of drug quality and sterilization effectiveness. Existing steam monitoring technologies generally rely on pressure and temperature sensors deployed at key nodes in the pipeline network, with monitoring methods focusing on independent reading of data from individual measuring points and threshold alarms. This discrete, point-based monitoring cannot reflect the overall state changes of steam during its transport through complex pipeline networks. Managers struggle to obtain crucial energy parameters such as the true specific enthalpy and entropy of steam as it reaches different end-user terminals, leading to frequent discrepancies between steam energy distribution and the heat demands of the production process, thus requiring improvement in energy utilization efficiency.

[0003] Steam dryness is a core indicator affecting heat transfer efficiency, and existing control methods mostly rely on feedback adjustment based on instantaneous measurements from dryness sensors. This approach is slow to respond to dryness fluctuations, has a simplistic adjustment mechanism, and lacks the ability to predict dryness change trends. Because it fails to incorporate historical operating data analysis, the adjustment process is often reactive rather than proactive. Under conditions of batch switching in veterinary drug production and frequent fluctuations in steam load, it is difficult to maintain continuous and stable steam quality, potentially causing process fluctuations in drying, extraction, and other processes that require constant heat loads.

[0004] The present invention aims to address how to deduce the overall thermodynamic state of the entire steam pipeline network from limited sensor data, and how to achieve forward-looking dynamic and precise control of steam dryness based on data trend analysis, so as to meet the stringent requirements of veterinary drug production for steam quality and efficient energy utilization. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a method for monitoring steam in veterinary drug production, comprising:

[0007] The raw steam data set is collected by multiple sensor nodes deployed in the steam pipeline network of the veterinary drug production workshop. The raw steam data set includes real-time steam pressure values, saturated steam temperature values, and instantaneous steam dryness values.

[0008] The real-time steam pressure value and the saturated steam temperature value are input into the steam state calculation model to generate the thermodynamic state parameters of each pipe section of the steam pipeline network. The thermodynamic state parameters include the steam specific enthalpy value, the steam entropy value, and the superheat value.

[0009] Based on the comparison and analysis between the instantaneous steam dryness value and historical dryness data, a periodic dryness compensation process is initiated. The periodic dryness compensation process generates a dryness adjustment command sequence based on the current steam operating conditions.

[0010] A steam energy distribution map is constructed using the aforementioned thermodynamic state parameters. Dynamic optimization is then performed in conjunction with the aforementioned dryness adjustment command sequence to generate steam supply strategies for different production stages.

[0011] Furthermore, the real-time steam pressure value and the saturated steam temperature value are input into the steam state calculation model to generate the thermodynamic state parameters of each pipe section of the steam pipeline network, including:

[0012] Establish a standard correspondence database of steam pressure and temperature, and match the collected real-time steam pressure values ​​with the saturated steam temperature values ​​to establish a correspondence.

[0013] When the deviation between the saturated steam temperature value and the real-time steam pressure value in the standard correspondence database exceeds the allowable range, the pipe section corresponding to the real-time steam pressure value and the saturated steam temperature value is marked as an abnormal pipe section.

[0014] For pipe sections that are not in abnormal condition, the corresponding theoretical value of saturation temperature, latent heat of vaporization and enthalpy of saturated water can be directly obtained by querying the steam property table based on the real-time steam pressure value.

[0015] The superheat value is calculated based on the difference between the saturated steam temperature value and the theoretical saturation temperature value.

[0016] Based on the latent heat of vaporization, the saturated water enthalpy, and the superheat value, the specific enthalpy of steam and the entropy of steam are calculated using thermodynamic formulas, and the calculation results of all pipe sections are summarized to form a pipeline-level thermodynamic state parameter table.

[0017] Furthermore, based on the comparative analysis of the instantaneous steam dryness value and historical dryness data, a periodic dryness compensation process is initiated, including:

[0018] Each sensor node is assigned an independent historical steam dryness data queue to continuously store steam dryness values ​​for a predetermined time period in the past.

[0019] Calculate the deviation between the currently collected instantaneous value of steam dryness and the average value in the corresponding historical data queue;

[0020] When the deviation exceeds the preset stability threshold, the steam dryness of the pipe section where the sensing node is located is determined to be in an abnormal fluctuation state.

[0021] Extract the thermodynamic state parameters of the pipe section under abnormal fluctuation, and analyze the changing trends of its superheat value and vapor enthalpy value;

[0022] Based on the changing trend, the direction and intensity of the dryness adjustment are determined, and the dryness adjustment instruction sequence containing the execution time point and the adjustment amount is generated.

[0023] Furthermore, a steam energy distribution map is constructed using the aforementioned thermodynamic state parameters, and dynamic optimization is performed in conjunction with the aforementioned dryness adjustment command sequence to generate steam supply strategies for different production stages, including:

[0024] The steam supply strategy includes a flow rate target, a pressure maintenance range, and a temperature fluctuation threshold.

[0025] Using the specific enthalpy of steam in each pipe segment of the steam pipeline network as the node weight and the connection relationship of the pipe segments as the edge, a directed weighted steam energy distribution map is constructed.

[0026] The steam energy distribution diagram identifies the terminal nodes that supply steam to different veterinary drug production stages. These terminal nodes include sterilization autoclave access points, drying equipment access points, and cleaning system access points.

[0027] Based on the process requirements specification of each terminal node, obtain the required steam quality benchmark, which includes the minimum allowable dryness value, the minimum required pressure value, and the maximum withstand temperature value.

[0028] The dryness adjustment command sequence is injected as a dynamic adjustment factor into the steam energy distribution diagram to simulate the changes in steam quality parameters of each pipe section after the command is executed.

[0029] A graph traversal algorithm is used, with the constraint that the steam quality benchmark of all terminal nodes is met, and with the goal of minimizing the total steam consumption, to perform iterative optimization. The opening setpoint of each pressure regulating valve, the compensation amount of each temperature compensator, and the operating frequency of each steam trap are calculated. The opening setpoint, compensation amount, and operating frequency together constitute the steam supply strategy.

[0030] Furthermore, it also includes: executing the steam supply strategy and simultaneously monitoring the steam quality feedback data of key nodes, calculating the deviation between the steam quality feedback data and the expected standard, and triggering the corresponding adjustment and correction process to update the dryness adjustment command sequence, including:

[0031] Secondary calibration sensors are deployed at the sterilizer inlet, the drying equipment inlet, and the cleaning system inlet to collect actual measured values ​​of steam pressure, temperature, and dryness.

[0032] The measured steam pressure is compared with the pressure maintenance range in the steam supply strategy, the measured temperature is compared with the temperature fluctuation threshold, and the measured dryness is compared with the expected dryness standard value to generate a multi-dimensional deviation vector.

[0033] When any component of the multi-dimensional deviation vector exceeds its corresponding tolerance limit, a global adjustment and correction process is triggered.

[0034] The global adjustment and correction process traces back to the upstream pipe section and the corresponding dryness adjustment command in the steam energy distribution diagram based on the specific components of the multi-dimensional deviation vector.

[0035] The adjustment parameters in the dryness adjustment command are corrected, and the corrected command is resubmitted to the dynamic optimization process as part of a new dryness adjustment command sequence.

[0036] Furthermore, it also includes steam demand forecasting and pre-adjustment steps based on production planning synchronization:

[0037] Obtain the production schedule plan of the veterinary drug production workshop, and parse the planned start and stop times and planned running time of each production equipment from the production schedule plan;

[0038] Based on the equipment type and product batch, the standard steam demand curve for the corresponding production stage is retrieved from the process database. The standard steam demand curve describes the changes in steam flow rate, pressure and dryness over time.

[0039] Based on the planned start-up and shutdown times, the standard steam demand curve is mapped onto the future time axis to generate a predictive steam demand load map for the entire workshop.

[0040] Before the predicted time point when steam demand changes significantly, a pre-adjustment command is injected into the periodic dryness compensation process to allow the steam system to transition to the expected demand state in advance.

[0041] Furthermore, the step of retrieving the standard steam demand curve for the corresponding production stage from the process database based on equipment type and product batch includes:

[0042] In the process database, an independent steam requirement file is established for each production stage of each veterinary drug product;

[0043] The steam demand profile is obtained by learning from historical batch production data and records the typical numerical range and variation cycle of steam flow rate, steam pressure and steam dryness during stable operation of the production stage.

[0044] When producing new products or batches without historical data, a temporary standard steam demand curve is generated by using the analogy method of similar products or the derivation method of process parameters.

[0045] The retrieved or generated standard steam demand curve is calibrated against the actual equipment parameters of the current production batch. Calibration factors include equipment volume, heat exchange area, and pipe diameter.

[0046] Furthermore, it also includes steps for monitoring the recovery of steam condensate and assessing its heat reuse:

[0047] Monitor the condensate temperature and condensate flow rate at the outlet of each steam trap in the steam pipeline network;

[0048] Calculate the total amount of residual heat energy carried by the condensate based on the condensate temperature and the condensate flow rate.

[0049] The total amount of residual heat energy is matched with the heat energy required to supplement the current production process to find process nodes that can utilize the residual heat energy.

[0050] If a matching process node exists, a condensate recovery path control instruction is generated, which is used to guide high-temperature condensate into the designated heat recovery device.

[0051] The steam supply strategy is updated synchronously, and the original steam supply of the corresponding link is appropriately reduced while ensuring process requirements are met.

[0052] Furthermore, the total amount of residual heat energy is matched with the heat energy required to supplement the current production process to identify process nodes where the residual heat energy can be utilized, including:

[0053] Obtain a list of thermal energy requirements for all currently running process nodes that require heating. The list includes the lower limit of the thermal energy requirement temperature, the total thermal energy requirement, and the thermal energy replenishment time window.

[0054] Process nodes whose condensate temperature is higher than the lower limit of the required thermal energy temperature are selected to form a set of potential usable nodes;

[0055] Calculate the thermal energy deficit of each node in the potentially available node set within the thermal energy replenishment time window;

[0056] The total amount of residual heat energy is allocated proportionally to the set of potentially usable nodes, with the allocation priority determined based on a comprehensive score of heat energy deficit and temperature matching degree.

[0057] To obtain the allocated process node, plan the condensate delivery pipeline route and calculate the original steam demand that the process node can reduce after allocating heat energy.

[0058] Furthermore, the present invention also includes a veterinary drug production steam monitoring system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the veterinary drug production steam monitoring method described above.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] By establishing a steam state calculation model and using real-time collected pressure and saturation temperature data as input, the specific enthalpy, entropy, and superheat of each pipe segment in the steam pipeline network are calculated. This process achieves mathematical reconstruction from discrete measuring points to the continuous pipeline network state field, generating a thermodynamic parameter map covering the entire transmission network. Based on this complete parameter field, the system can accurately characterize the spatial distribution and attenuation of steam energy, accurately identify inefficient or abnormal pipe segments, and make it possible to formulate differentiated steam supply strategies that match the actual thermodynamic state for the heat demand of different production stages. This changes the extensive mode of allocating steam based on experience or local data.

[0061] Initiating a periodic dryness compensation process based on a comparative analysis of the current instantaneous dryness value and historical data signifies that the regulation decision integrates real-time status and long-term operating patterns. This process generates a series of dryness regulation commands arranged over time, rather than a single command. This design upgrades simple feedback control to composite control incorporating feedforward prediction. The system can detect deviations in the current value and, based on regulation experience from similar historical operating conditions, proactively deploy subsequent multi-step regulation actions. This gives dryness regulation adaptability and predictability, smoothing the dryness fluctuation curve caused by load changes and providing more stable and reliable steam quality for downstream process equipment in production environments with frequent operating condition variations. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating the steps of the veterinary drug production steam monitoring method described in this invention.

[0063] Figure 2 A flowchart generated for the steam supply strategy;

[0064] Figure 3 To adjust the relationship between command intensity and dryness deviation;

[0065] Figure 4 This is a monitoring curve for steam dryness compensation.

[0066] Figure 5 This is a graph showing the real-time monitoring of condensate temperature and flow rate in a steam trap. Detailed Implementation

[0067] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0068] See Figure 1 The system collects raw steam data, including real-time steam pressure, saturated steam temperature, and instantaneous steam dryness, through multiple sensor nodes deployed in the steam pipeline network of the veterinary drug production workshop. The collected real-time steam pressure and saturated steam temperature values ​​are then input into a steam state calculation model. This model generates thermodynamic state parameters for each section of the steam pipeline network, including steam specific enthalpy, steam entropy, and superheat. Based on this, the system initiates a periodic dryness compensation process by comparing the instantaneous steam dryness value with historical dryness data. This process generates a set of dryness adjustment command sequences based on the current steam conditions. Finally, using the thermodynamic state parameters generated in the preceding steps, a steam energy distribution map reflecting the system state is constructed. Combined with the generated dryness adjustment command sequences, the entire pipeline network is dynamically optimized to derive customized steam supply strategies for different stages of veterinary drug production, achieving precise allocation of steam resources.

[0069] In one embodiment of the invention, a standard correspondence database of steam pressure and temperature, containing steam property parameters, is established. This database records the theoretical temperature values ​​of saturated steam at different pressures. In a specific implementation, sensors deployed on the steam pipeline collect a real-time steam pressure value of 0.5 MPa for a certain pipe section, and simultaneously collect a saturated steam temperature value of 155.0 degrees Celsius at the same point. The system inputs the collected real-time steam pressure value of 0.5 MPa and the saturated steam temperature value of 155.0 degrees Celsius into the standard correspondence database for matching. In a specific implementation, the standard correspondence database shows that the theoretical saturated temperature value corresponding to a pressure of 0.5 MPa is 151.8 degrees Celsius. In a specific implementation, the system calculates the deviation between the collected saturated steam temperature value of 155.0 degrees Celsius and the theoretical temperature value of 151.8 degrees Celsius. In a specific implementation, the system's preset allowable temperature deviation range is ±2.0 degrees Celsius. The calculated deviation is positive 3.2 degrees Celsius. This deviation exceeds the allowable range, and the system marks the pipe section corresponding to this real-time steam pressure value and saturated steam temperature value as an abnormal pipe section.

[0070] In some embodiments, for steam pipe sections marked as non-abnormal, the system directly queries the steam property table published by the International Association for the Properties of Water and Steam (IAWA) based on the real-time steam pressure values ​​collected by the sensors of that pipe section to obtain the corresponding theoretical saturation temperature, latent heat of vaporization, and saturated water enthalpy. In a specific implementation, for a non-abnormal pipe section with a pressure of 0.8 MPa, the steam property table yields a theoretical saturation temperature of 170.4 degrees Celsius, a latent heat of vaporization of 2046.7 kJ / kg, and a saturated water enthalpy of 720.9 kJ / kg. In this specific implementation, the saturated steam temperature collected by the sensors of that pipe section is 172.1 degrees Celsius. Based on the difference between the saturated steam temperature of 172.1 degrees Celsius and the theoretical saturation temperature of 170.4 degrees Celsius, the superheat value is calculated, i.e., the superheat value equals 172.1 degrees Celsius minus 170.4 degrees Celsius, resulting in 1.7 degrees Celsius.

[0071] In practical implementation, based on the retrieved latent heat of vaporization, saturated water enthalpy, and calculated superheat value, the specific enthalpy and entropy of steam are calculated using thermodynamic formulas. It can be understood that the calculation process can refer to the following form: for superheated steam, its specific enthalpy is composed of the saturated water enthalpy, latent heat of vaporization, and the accumulated heat of the superheated portion. In some embodiments, the following relationship can be used for estimation calculation:

[0072]

[0073] Where: symbol This represents the specific enthalpy of the steam to be determined, expressed in kilojoules per kilogram; symbol This represents the enthalpy of saturated water obtained from the query, in kilojoules per kilogram; symbol Represents the steam dryness fraction, assumed to be 1 here (i.e., dry saturated steam begins to superheat); symbol This represents the latent heat of vaporization obtained from the query, in kilojoules per kilogram; symbol The isobaric specific heat capacity of superheated steam, measured in kilojoules per kilogram of degree Celsius; symbol This represents the calculated superheat value, expressed in degrees Celsius. The calculation of entropy follows similar thermodynamic principles. In practice, the query and calculation steps are repeatedly performed on all non-abnormal pipe sections in the steam network. The calculation results of the steam specific enthalpy, steam entropy, and superheat values ​​for all pipe sections are summarized to form a network-level thermodynamic state parameter table containing the thermal states of all nodes in the network.

[0074] In practical implementation, a periodic dryness compensation process is initiated based on the comparative analysis of instantaneous steam dryness values ​​and historical dryness data. An independent historical steam dryness data queue is set up for each sensor node equipped with a steam dryness sensor. In practice, this data queue continuously stores steam dryness values ​​collected at 1-minute intervals over the past 30 minutes, totaling 30 historical data points. At the end of the current collection cycle, the system obtains the latest instantaneous steam dryness value of sensor node A as 0.92. The system then calculates the average of the currently collected instantaneous steam dryness value of 0.92 and all stored values ​​in the corresponding historical data queue. Assuming the average dryness value over the past 30 minutes in the historical data queue is 0.95, the deviation is calculated as follows: The calculated result is approximately 3.16%.

[0075] In practical implementation, the system's preset steam dryness stability threshold is 2%. In practice, the currently calculated deviation of 3.16% consistently exceeds the preset stability threshold. This means that the deviation is greater than 2% for three consecutive acquisition cycles. When this condition is met, the system determines that the steam dryness of the pipe section where sensor node A is located is in an abnormal fluctuation state. In practical implementation, the system extracts the thermodynamic state parameters of the pipe section in the abnormal fluctuation state (i.e., the pipe section where sensor node A is located) from the generated pipe network-level thermodynamic state parameter table, including superheat and steam enthalpy. In practical implementation, the system analyzes the changing trends of superheat and steam enthalpy in this pipe section over a period of time. For example, if the analysis finds that the superheat is decreasing and the steam enthalpy is also decreasing simultaneously, it indicates that the steam quality is deteriorating and may be carrying more moisture. In practical implementation, based on the changing trends of superheat and steam enthalpy, the direction of dryness adjustment is determined to be to increase dryness, and the adjustment intensity is determined comprehensively based on the deviation and historical fluctuation amplitudes.

[0076] See Figure 2 In one embodiment of the present invention, the flow rate setting target is the unit-time steam mass flow rate value set for the steam supply terminals of different production stages; the pressure maintenance range is the upper and lower limit range of the allowable steam pressure at the terminal steam supply point; and the temperature fluctuation threshold is the maximum absolute value of the allowable deviation of the steam supply temperature from the set value. In a specific implementation, a directed weighted steam energy distribution map is constructed using the steam specific enthalpy value of each pipe segment in the steam pipeline network as the node weight and the pipe segment connection relationship as the edge. In a specific implementation, the pipeline network topology is abstracted as a graph model. Each pressure measurement point or key connection point in the pipeline network is regarded as a node. The weight assigned to the node is the steam specific enthalpy value obtained from the pipeline network-level thermodynamic state parameter table. The physical pipes between nodes are regarded as directed edges, and the direction of the edge represents the actual flow direction of the steam.

[0077] The steam energy distribution map identifies the terminal nodes supplying steam to different stages of veterinary drug production. In some embodiments, terminal nodes include sterilizer access points, drying equipment access points, and cleaning system access points. In specific implementations, each terminal node corresponds to a specific physical connection point in the pipeline network connected to the production equipment on the steam energy distribution map. In specific implementations, the steam quality standards for each terminal node are obtained by parsing from the process requirements specification. The steam quality standards include the minimum allowable dryness value, the minimum required pressure value, and the maximum tolerable temperature value. For example, the process requirements specification for the sterilizer access point specifies a minimum allowable dryness value of 0.95, a minimum required pressure value of 0.6 MPa, and a maximum tolerable temperature value of 165 degrees Celsius; the minimum allowable dryness value for the drying equipment access point is 0.90, the minimum required pressure value is 0.4 MPa, and the maximum tolerable temperature value is 150 degrees Celsius.

[0078] In practical implementation, the dryness fraction adjustment command sequence is injected as a dynamic adjustment factor into the steam energy distribution map. Each dryness fraction adjustment command is associated with one or more regulating devices in the pipeline network, and its execution effect is to change the steam dryness fraction distribution in the downstream region of the command's point of action. In practice, simulating the execution of the dryness fraction adjustment command sequence means adjusting the calculation logic of the steam state parameters of the corresponding nodes and the downstream affected edges within the mathematical model of the steam energy distribution map according to the command content. After simulating the execution of the commands, the system recalculates the steam quality parameters of each pipe segment node in the diagram, especially dryness fraction and pressure, and these parameters will change accordingly.

[0079] A graph traversal algorithm is employed, with the constraint that the steam quality benchmarks of all terminal nodes are met, and the objective of minimizing total steam consumption through iterative optimization. In specific implementation, the constraints are manifested in the verification of real-time simulation parameters for each terminal node in the graph: the calculated steam dryness fraction must be greater than or equal to its minimum allowable dryness fraction, the calculated steam pressure must be greater than or equal to its minimum required pressure, and the calculated steam temperature must be less than or equal to its maximum tolerable temperature. In practice, minimizing total steam consumption can be achieved by optimizing the overall steam flow distribution, particularly reducing unnecessary emissions and leaks, and optimizing boiler load. In some embodiments, the graph traversal algorithm can be a variant of Dijkstra's algorithm, finding the lowest-cost path combination from the steam source to all terminal nodes while satisfying all terminal node constraints. Here, "cost" is related to the product of the steam flow rate and specific enthalpy of the pipe segment, and their sum approximately represents the total system energy consumption. The iterative optimization process repeatedly tries different combinations of pressure regulating valve opening settings, temperature compensator compensation amounts, and steam trap operating frequencies.

[0080] In practical implementation, through iterative optimization calculations, a set of operating parameters that minimizes energy consumption while meeting all process requirements is ultimately output. The calculation process may involve an evaluation function. Optionally, a function for evaluating the degree to which steam quality is satisfied at each terminal node during the optimization process can be categorized as follows:

[0081]

[0082] Where: symbol Represents the overall deviation of steam quality; it is a dimensionless assessment value. (Symbol: ...) Represents the total number of terminal nodes; symbol Representing the The negative deviation between the simulated steam dryness value and the minimum allowable dryness value at each terminal node; symbol Representing the The simulated steam pressure values ​​at each terminal node show a negative deviation from the minimum required pressure values; (symbol) Representing the Positive deviation between the simulated steam temperature value and the maximum withstand temperature value at each terminal node; symbol , , These represent the weighting coefficients corresponding to the steam dryness, pressure, and temperature deviation terms, respectively. These coefficients can be set according to the importance of different end processes. One of the goals of iterative optimization is to make... The value is always zero, meaning all constraints are satisfied. In practice, the opening setpoint of each pressure regulating valve, the compensation amount of each temperature compensator, and the operating frequency of each steam trap are calculated. The opening setpoint, compensation amount, and operating frequency together constitute the steam supply strategy.

[0083] In one embodiment of the invention, secondary calibration sensors are deployed at the sterilizer inlet, the drying equipment inlet, and the cleaning system inlet. These secondary calibration sensors are independent of the sensors in the main data acquisition system of the steam pipeline network. In specific implementation, after the set control cycle ends, the secondary calibration sensors collect the actual measured values ​​of steam pressure, steam temperature, and steam dryness at the sterilizer inlet, drying equipment inlet, and cleaning system inlet. For example, the secondary calibration sensor at the sterilizer inlet collects a measured steam pressure of 0.61 MPa, a measured steam temperature of 162 degrees Celsius, and a measured steam dryness of 0.94.

[0084] In practice, the measured steam pressure is compared with the pressure maintenance range specified in the steam supply strategy. The pressure maintenance range set for the autoclave inlet in the steam supply strategy is 0.60 MPa to 0.65 MPa. In practice, the measured steam temperature is compared with the temperature fluctuation threshold set in the steam supply strategy. The temperature fluctuation threshold set for the autoclave inlet in the steam supply strategy is ±5 degrees Celsius, with a set temperature of 160 degrees Celsius as the baseline, and the allowable temperature range is 155 degrees Celsius to 165 degrees Celsius. In practice, the measured steam dryness is compared with the expected dryness standard value given in the steam supply strategy. The expected dryness standard value is the dryness value expected to be delivered to the inlet based on the steam supply strategy; for example, the expected dryness standard value for the autoclave inlet is 0.96.

[0085] In practical implementation, a multi-dimensional deviation vector is generated through the above comparison operations. It can be understood that a multi-dimensional deviation vector is a mathematical vector, where each component represents the deviation of the actual measured value of a compared parameter from the expected standard or allowable range. In practice, generating the multi-dimensional deviation vector involves calculating the deviations. For example, for the sterilizer inlet point, the measured steam pressure of 0.61 MPa is within the pressure maintenance range of 0.60 MPa to 0.65 MPa, so the pressure deviation component is recorded as 0. The measured steam temperature of 162 degrees Celsius, with a set temperature of 160 degrees Celsius as the baseline, has a deviation of +2 degrees Celsius, which is within the temperature fluctuation threshold of ±5 degrees Celsius, so the temperature deviation component is recorded as 0. The measured steam dryness is 0.94, and the expected dryness standard value is 0.96, so the dryness deviation is -0.02, and the dryness deviation component is recorded as -0.02. Thus, a multi-dimensional deviation vector is generated, corresponding to the pressure deviation component, temperature deviation component, and dryness deviation component, respectively.

[0086] In practice, each deviation dimension component has a preset upper tolerance limit. For example, the upper tolerance limit for the pressure deviation component is 0.02 MPa above the limit, the upper tolerance limit for the temperature deviation component is 0.5 degrees Celsius above the threshold, and the upper tolerance limit for the dryness deviation component is an absolute value of 0.01. When any dimension component in the multi-dimensional deviation vector exceeds its corresponding upper tolerance limit, a global adjustment and correction process is triggered. In the example of the sterilizer inlet point, the absolute value of the dryness deviation component -0.02 is greater than its upper tolerance limit of 0.01, thus triggering the global adjustment and correction process.

[0087] The global adjustment and correction process traces back to the upstream pipe segments and corresponding dryness adjustment commands in the steam energy distribution map based on the specific components of the multi-dimensional deviation vector. In some embodiments, the back-tracing first locates the terminal node where the deviation occurs in the steam energy distribution map, i.e., the sterilizer inlet point. Then, starting from this terminal node, the search proceeds in the reverse direction of the directed edge (i.e., upstream of the steam flow direction) to analyze the state parameters of each upstream pipe segment in the steam supply strategy and the dryness adjustment commands that affect the steam dryness of these pipe segments. For example, the tracing reveals that the direct upstream of the sterilizer inlet point is a steam distribution cylinder node, and the steam dryness of the steam distribution cylinder node is affected by the dryness adjustment command executed on a main steam pipeline upstream of it. The adjustment direction of this command is to increase the condensate frequency.

[0088] The adjustment parameters in the dryness adjustment command are revised. In practice, the revision is based on the magnitude and direction of the multi-dimensional deviation vector. For example, the multi-dimensional deviation vector at the sterilizer inlet point shows low dryness (negative deviation), while the original upstream dryness adjustment command aimed to increase the condensation frequency to improve dryness, but the actual effect did not meet expectations. In practice, the revision process increases the adjustment intensity in this dryness adjustment command; for example, the adjustment parameter "increase condensation frequency by 20%" is modified to "increase condensation frequency by 35%". The revised command is then resubmitted to the dynamic optimization process as part of a new dryness adjustment command sequence to calculate and generate an updated steam supply strategy that better matches the expected steam parameters at the terminal nodes.

[0089] See Figure 3 This is a graph showing the relationship between the intensity of the adjustment command and the dryness deviation. It illustrates the correspondence between the dryness deviation value and the intensity of the adjustment command during the global adjustment and correction process, and is a key analytical graph for the veterinary drug production steam monitoring system. When the dryness deviation value is in the range of -0.04 to -0.02, the adjustment intensity fluctuates significantly between 20% and 35%, reflecting that the system dynamically adjusts the adjustment intensity according to the degree of deviation when the dryness is significantly low. When the dryness deviation value is close to -0.01 to -0.005, the adjustment intensity stabilizes at 24%–25%, indicating that the system adopts a more conservative adjustment strategy when the deviation is small. The adjustment intensity is generally low in the early cycles, while it increases in the later cycles, indicating that the system gradually optimizes the adjustment strategy during operation to cope with continuous dryness deviation. The nearly horizontal trend line shows that during global adjustment and correction, the system does not simply linearly amplify the adjustment intensity according to the deviation, but maintains a core adjustment benchmark to avoid over-adjustment that could lead to system instability.

[0090] In one embodiment of the present invention, a production schedule for a veterinary drug production workshop is obtained. In specific implementations, the production schedule is an electronic document or database record that lists the product batches planned for production in the workshop over a future period, the equipment used, and the planned operating times. The planned start-up and shutdown times and planned operating durations for each production piece of equipment are extracted from the production schedule. For example, the planned start-up time for sterilizer K-101 is "tomorrow 08:00", the planned shutdown time is "tomorrow 16:00", and the planned operating duration is 8 hours; the planned start-up time for spray drying tower D-201 is "tomorrow 10:00", the planned shutdown time is "tomorrow 20:00", and the planned operating duration is 10 hours.

[0091] In practice, the standard steam demand curve for the corresponding production stage is retrieved from the process database based on the equipment type and product batch. In this implementation, the process database is a relational database that stores historical production parameters. For example, for a record with equipment type "steam sterilizer" and product batch "Type A veterinary vaccine - batch 20250301," the process database stores the standard steam demand curve for its sterilization stage. The standard steam demand curve describes the changes in steam flow rate, steam pressure, and steam dryness over time during stable operation at that production stage. In some embodiments, the standard steam demand curve is stored as a set of time-parameter value pairs or represented as a specific mathematical function.

[0092] Using planned start-up and shutdown times as a baseline, the standard steam demand curve is mapped onto a future time axis to generate a predictive steam demand load map for the entire workshop. In practice, the mapping process aligns the zero point of the standard steam demand curve's time axis with the planned start-up time of the production equipment. Then, the curve is stretched or compressed on the time axis to match the planned operating duration. Finally, the predicted steam demand of all equipment within the workshop during their respective operating periods is superimposed. For example, Table 1 shows a simplified segment of the predictive steam demand load map, depicting the predicted total steam flow demand of the workshop over a future time period.

[0093] Table 1: Predictive Steam Demand Load Chart for the Entire Workshop

[0094]

[0095] Before the predicted time point when steam demand changes significantly, a pre-adjustment command is injected into the periodic dryness compensation process. In specific implementation, the system analyzes the steam demand load map of the entire workshop to identify the time points when the total steam flow, pressure, or dryness demand changes significantly, such as 09:30 and 10:00 in Table 1. It can be understood that the judgment of significant change is based on a preset threshold, such as the change rate of total steam flow exceeding 15%. In specific implementation, in order to ensure that the state of the steam system (such as pipeline pressure distribution and steam storage capacity) transitions to the expected demand in advance and avoids system response lag when demand changes suddenly, the system will issue a pre-adjustment command to the periodic dryness compensation process within a time window before the predicted change point. For example, for the predicted demand of total steam flow increasing from 1500 kg / h to 1850 kg / h at 10:00, the system injects a pre-adjustment command into the periodic dryness compensation process at 09:40. This command may include parameters for starting the standby boiler in advance or fine-tuning the main regulating valve of the pipeline network.

[0096] In practice, based on equipment type and product batch, standard steam demand curves for the corresponding production stage are retrieved from the process database. Within the process database, an independent steam demand profile is established for each production stage of each veterinary drug product. For example, an independent steam demand profile is created for the "fermentation broth sterilization" stage of "Type A veterinary vaccine," and another independent steam demand profile is created for the "spray drying" stage of "Type B veterinary disinfectant." The steam demand profiles are learned from historical batch production data, recording the typical numerical ranges and variation cycles of steam flow rate, steam pressure, and steam dryness during stable operation of each production stage. In practice, the typical numerical ranges stored in the profiles are obtained through statistical analysis of data from multiple historical stable production batches. For example, during the stable "fermentation broth sterilization" stage, the steam flow rate is typically maintained at 1200±50 kg / h, the steam pressure at 0.60±0.02 MPa, and the steam dryness at 0.96±0.01, with a variation cycle roughly corresponding to the heating, holding, and cooling stages of equipment operation.

[0097] When producing new products or batches without historical data, a temporary standard steam demand curve is generated using the analogy method of similar products or the process parameter derivation method. In some embodiments, the analogy method of similar products is used to produce a "Type C veterinary vaccine" with a sterilization process similar to that of "Type A veterinary vaccine". The system can call up the steam demand file of the "Type A veterinary vaccine - sterilization" stage, scale it proportionally based on the equipment capacity difference, and generate a temporary standard steam demand curve for the "Type C veterinary vaccine - sterilization" stage. In other embodiments, the process parameter derivation method is applied to a completely new drying process. The system can estimate the steam demand using the theoretical heat balance formula based on the target temperature, material quantity, specific heat capacity, and other parameters set for the process, thereby generating a temporary standard steam demand curve. Optionally, a simplified heat balance estimation formula is as follows:

[0098]

[0099] Where: symbol This represents the estimated thermal power required for the process, expressed in kilowatts (kW); symbol Represents the mass flow rate of the material being processed, measured in kilograms per second; symbol This represents the average specific heat capacity of a material, expressed in kilojoules per kilogram per degree Celsius; symbol Represents the target temperature for material handling, in degrees Celsius; symbol This represents the initial temperature of the material, expressed in degrees Celsius. Combined with the latent heat or enthalpy of the steam, the approximate steam flow rate requirement can be estimated.

[0100] The standard steam demand curve, retrieved or generated, is calibrated against the actual equipment parameters of the current production batch. In practice, calibration factors include equipment volume, heat exchange area, and pipe diameter. For example, the standard steam demand curve retrieved from historical data might be based on a 5-cubic-meter autoclave, while the current batch uses an 8-cubic-meter autoclave. In practice, the calibration process will amplify and adjust the steam flow time curve accordingly based on the ratio of the equipment volume (8 / 5). If the heat exchange area of ​​the current batch differs from historical equipment, or if differences in connecting pipe diameters lead to changes in flow resistance, the pressure demand portion of the standard steam demand curve will also be corrected accordingly to generate a standard steam demand curve that better reflects the current production situation.

[0101] See Figure 4This is a steam dryness compensation monitoring curve, showing the relationship between the actual dryness value, target dryness value, and compensation value over time during the periodic dryness compensation process. The actual dryness value is mostly lower than the target value, especially during the 1–2 hour and 4.5–6 hour periods, where the deviation is most significant, triggering the system's compensation mechanism. During the 3 hour and 7–8 hour periods, the actual dryness briefly exceeds the target value; at this time, the compensation value is close to 0, indicating that the system reduces intervention when the dryness meets the target. The compensation value and the actual dryness deviation have an inverse relationship: when the actual dryness is lower than the target, the compensation value is positive, and the system increases the dryness by increasing the condensation frequency; when the actual dryness is higher than the target, the compensation value is negative, and the system appropriately reduces intervention. Overall, the system can effectively control the actual dryness near the target value, with a deviation within ±0.1, meeting the stability requirements of steam dryness for veterinary drug production.

[0102] In one embodiment of the invention, the condensate temperature and flow rate at the outlet of each steam trap in the steam pipeline network are monitored. In specific implementations, temperature sensors and flow meters are installed after steam traps at key branch ends of the steam pipeline network, heat exchanger outlets, and other locations to collect condensate temperature and flow rate data. For example, the condensate temperature at the outlet of steam trap SV-101 is monitored to be 85 degrees Celsius, and the condensate flow rate is 0.8 tons per hour; the condensate temperature at the outlet of steam trap SV-102 is monitored to be 92 degrees Celsius, and the condensate flow rate is 1.2 tons per hour.

[0103] In specific implementations, the total residual heat energy carried by the condensate is calculated based on the condensate temperature and condensate flow rate. In specific implementations, the calculation of the total residual heat energy is based on the sensible heat of the condensate relative to the ambient or reuse reference temperature. In some embodiments, the calculation may be based on the following thermodynamic relationship: Total residual heat energy carried by the condensate. The following formula can be used for estimation:

[0104]

[0105] Where: symbol Represents the total residual heat energy carried by condensate at all monitoring points per unit time, expressed in kilowatts; symbol Represents the total number of monitored steam traps; symbol Representing the The condensate volumetric flow rate monitored at the outlet of each steam trap is expressed in cubic meters per hour; symbol Represents the density of condensed water, measured in kilograms per cubic meter; symbol The specific heat capacity at constant pressure of condensate, measured in kilojoules per kilogram of degree Celsius; symbol Representing the The condensate temperature monitored at the outlet of each steam trap is in degrees Celsius; symbol This represents a selected reference temperature, such as the inlet water temperature of a condensate recycling system or the ambient temperature, in degrees Celsius. In practical implementation, taking monitoring points SV-101 and SV-102 as examples, assuming a condensate density of 1000 kg / m³, a specific heat capacity of 4.2 kJ / kg / degree Celsius, and a reference temperature of 20 degrees Celsius, the total residual heat energy carried by the condensate at the outlets of these two steam traps per unit time can be calculated.

[0106] The process involves matching the total residual heat energy with the current production process's required heat energy to identify process nodes where residual heat energy can be utilized. In practice, a list of heat energy requirements for all currently operating process nodes that require heating is obtained. This list is retrieved from the Production Execution System (MES) or Process Control System and includes the lower limit of the required heat energy temperature, the total required heat energy, and the heat energy replenishment time window. For example, the heat energy requirement list for a washing hot water preparation tank might include: a lower limit of the required heat energy temperature of 70 degrees Celsius, a total required heat energy of 5000 MJ, and a heat energy replenishment time window within the next 2 hours. Similarly, the heat energy requirement list for a raw material preheating jacket might include: a lower limit of the required heat energy temperature of 60 degrees Celsius, a total required heat energy of 2000 MJ, and a heat energy replenishment time window within the next 1 hour.

[0107] In practice, process nodes with condensate temperatures higher than the lower limit of the required thermal energy are selected to form a set of potential usable nodes. For example, the lower limit of the required thermal energy for the washing hot water preparation tank is 70 degrees Celsius, and the lower limit of the required thermal energy for the raw material preheating jacket is 60 degrees Celsius. The monitored condensate temperature ranges from 85 degrees Celsius to 92 degrees Celsius, which is higher than the lower limit of the required thermal energy for these two process nodes. Therefore, both the washing hot water preparation tank and the raw material preheating jacket are included in the set of potential usable nodes.

[0108] In specific implementations, the heat energy deficit for each node within the potentially available node set within the heat energy replenishment time window is calculated. In specific implementations, the heat energy deficit is the difference between the total heat required for a process node to complete its heating process within the heat energy replenishment time window and the currently stored or supplied heat. In specific implementations, the total residual heat energy is allocated proportionally to the potentially available node set. In some embodiments, the allocation priority is determined based on a comprehensive score of the heat energy deficit and temperature matching degree. Temperature matching degree can be defined as the difference between the condensate temperature and the lower limit of the heat energy required by the process node; a smaller difference indicates a higher temperature matching degree and more rational utilization of heat energy. It is understood that the allocation process needs to ensure that the total allocated heat energy does not exceed the total available residual heat energy. In specific implementations, condensate delivery pipeline paths are planned for the process nodes receiving heat energy allocation, and the reduction in the original steam demand of the process nodes after heat energy allocation is calculated. For example, it is calculated that allocating a portion of the heat energy to the washing hot water preparation tank can reduce its required fresh steam replenishment by 15%.

[0109] If a matching process node exists, a condensate recovery path control instruction is generated. In practice, this instruction guides high-temperature condensate into a designated heat recovery unit. For example, the generated instruction opens the electric three-way valve V-201 connecting the outlet of the SV-101 steam trap to the inlet of the washing hot water preparation tank, and sets the frequency of the condensate pump P-301 to 35 Hz. The steam supply strategy is updated synchronously, appropriately reducing the initial steam supply to the corresponding stage while ensuring process requirements are met.

[0110] See Figure 5 This is a real-time monitoring chart of condensate temperature and flow rate for two key steam traps (SV-101 and SV-102), showing the condensate temperature and flow rate trends over 24 hours during the condensate recovery monitoring and data acquisition phase. The condensate temperature and flow rate of both steam traps exhibit highly synchronized fluctuations, indicating that changes in steam load are the primary driving factor for changes in condensate conditions. The temperature and flow rate of SV-102 are consistently higher than those of SV-101, indicating that the steam quality and load in its pipe section are higher, making it the main source of condensate. Temperature and flow rate peak around 7 hours, then trough around 16 hours, and subsequently gradually recover, consistent with the production scheduling patterns of the veterinary drug production workshop. The trough at 16 hours may correspond to shift changes or equipment downtime, leading to a decrease in steam load.

[0111] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for monitoring steam in veterinary drug production, characterized in that, The method includes: The raw steam data set is collected by multiple sensor nodes deployed in the steam pipeline network of the veterinary drug production workshop. The raw steam data set includes real-time steam pressure values, saturated steam temperature values, and instantaneous steam dryness values. The real-time steam pressure value and the saturated steam temperature value are input into the steam state calculation model to generate the thermodynamic state parameters of each pipe section of the steam pipeline network. The thermodynamic state parameters include the steam specific enthalpy value, the steam entropy value, and the superheat value. Based on the comparison and analysis between the instantaneous steam dryness value and historical dryness data, a periodic dryness compensation process is initiated. The periodic dryness compensation process generates a dryness adjustment command sequence based on the current steam operating conditions. A steam energy distribution map is constructed using the aforementioned thermodynamic state parameters, and dynamic optimization is performed in conjunction with the aforementioned dryness adjustment command sequence to generate steam supply strategies for different production stages. The real-time steam pressure and saturated steam temperature values ​​are input into the steam state calculation model to generate the thermodynamic state parameters of each pipe section of the steam pipeline network, including: Establish a standard correspondence database of steam pressure and temperature, and match the collected real-time steam pressure values ​​with the saturated steam temperature values ​​to establish a correspondence. When the deviation between the saturated steam temperature value and the real-time steam pressure value in the standard correspondence database exceeds the allowable range, the pipe section corresponding to the real-time steam pressure value and the saturated steam temperature value is marked as an abnormal pipe section. For pipe sections that are not in abnormal condition, the corresponding theoretical value of saturation temperature, latent heat of vaporization and enthalpy of saturated water can be directly obtained by querying the steam property table based on the real-time steam pressure value. The superheat value is calculated based on the difference between the saturated steam temperature value and the theoretical saturation temperature value. Based on the latent heat of vaporization, the saturated water enthalpy, and the superheat value, the specific enthalpy of steam and the entropy of steam are calculated using thermodynamic formulas, and the calculation results of all pipe sections are summarized to form a pipeline-level thermodynamic state parameter table.

2. The method for monitoring steam in veterinary drug production according to claim 1, characterized in that, Based on the comparative analysis of the instantaneous steam dryness value and historical dryness data, a periodic dryness compensation process is initiated, including: Each sensor node is assigned an independent historical steam dryness data queue to continuously store steam dryness values ​​for a predetermined time period in the past. Calculate the deviation between the currently collected instantaneous value of steam dryness and the average value in the corresponding historical data queue; When the deviation exceeds the preset stability threshold, the steam dryness of the pipe section where the sensing node is located is determined to be in an abnormal fluctuation state. Extract the thermodynamic state parameters of the pipe section under abnormal fluctuation, and analyze the changing trends of its superheat value and vapor enthalpy value; Based on the changing trend, the direction and intensity of the dryness adjustment are determined, and the dryness adjustment instruction sequence containing the execution time point and the adjustment amount is generated.

3. The method for monitoring steam in veterinary drug production according to claim 2, characterized in that, A steam energy distribution map is constructed using the aforementioned thermodynamic state parameters. Dynamic optimization is then performed using the aforementioned dryness adjustment command sequence to generate steam supply strategies for different production stages, including: The steam supply strategy includes a flow rate target, a pressure maintenance range, and a temperature fluctuation threshold. Using the specific enthalpy of steam in each pipe segment of the steam pipeline network as the node weight and the connection relationship of the pipe segments as the edge, a directed weighted steam energy distribution map is constructed. The steam energy distribution diagram identifies the terminal nodes that supply steam to different veterinary drug production stages. These terminal nodes include sterilization autoclave access points, drying equipment access points, and cleaning system access points. Based on the process requirements specification of each terminal node, obtain the required steam quality benchmark, which includes the minimum allowable dryness value, the minimum required pressure value, and the maximum withstand temperature value. The dryness adjustment command sequence is injected as a dynamic adjustment factor into the steam energy distribution diagram to simulate the changes in steam quality parameters of each pipe section after the command is executed. A graph traversal algorithm is used, with the constraint that the steam quality benchmark of all terminal nodes is met, and with the goal of minimizing the total steam consumption, to perform iterative optimization. The opening setpoint of each pressure regulating valve, the compensation amount of each temperature compensator, and the operating frequency of each steam trap are calculated. The opening setpoint, compensation amount, and operating frequency together constitute the steam supply strategy.

4. The method for monitoring steam in veterinary drug production according to claim 3, characterized in that, Also includes: The steam supply strategy is executed while simultaneously monitoring steam quality feedback data at key nodes. Deviation calculations are performed between the steam quality feedback data and the expected standard, triggering the corresponding adjustment and correction process to update the dryness adjustment command sequence, including: Secondary calibration sensors are deployed at the sterilizer inlet, the drying equipment inlet, and the cleaning system inlet to collect actual measured values ​​of steam pressure, temperature, and dryness. The measured steam pressure is compared with the pressure maintenance range in the steam supply strategy, the measured temperature is compared with the temperature fluctuation threshold, and the measured dryness is compared with the expected dryness standard value to generate a multi-dimensional deviation vector. When any component of the multi-dimensional deviation vector exceeds its corresponding tolerance limit, a global adjustment and correction process is triggered. The global adjustment and correction process traces back to the upstream pipe section and the corresponding dryness adjustment command in the steam energy distribution diagram based on the specific components of the multi-dimensional deviation vector. The adjustment parameters in the dryness adjustment command are corrected, and the corrected command is resubmitted to the dynamic optimization process as part of a new dryness adjustment command sequence.

5. The method for monitoring steam in veterinary drug production according to claim 4, characterized in that, It also includes steam demand forecasting and pre-adjustment steps based on production planning synchronization: Obtain the production schedule plan of the veterinary drug production workshop, and parse the planned start and stop times and planned running time of each production equipment from the production schedule plan; Based on the equipment type and product batch, the standard steam demand curve for the corresponding production stage is retrieved from the process database. The standard steam demand curve describes the changes in steam flow rate, pressure and dryness over time. Based on the planned start-up and shutdown times, the standard steam demand curve is mapped onto the future time axis to generate a predictive steam demand load map for the entire workshop. Before the predicted time point when steam demand changes significantly, a pre-adjustment command is injected into the periodic dryness compensation process to allow the steam system to transition to the expected demand state in advance.

6. The method for monitoring steam in veterinary drug production according to claim 5, characterized in that, The step of retrieving the standard steam demand curve for the corresponding production stage from the process database based on equipment type and product batch includes: In the process database, an independent steam requirement file is established for each production stage of each veterinary drug product; The steam demand profile is obtained by learning from historical batch production data and records the typical numerical range and change cycle of steam flow rate, steam pressure and steam dryness during stable operation of the production stage. When producing new products or batches without historical data, a temporary standard steam demand curve is generated by using the analogy method of similar products or the derivation method of process parameters. The retrieved or generated standard steam demand curve is calibrated against the actual equipment parameters of the current production batch. Calibration factors include equipment volume, heat exchange area, and pipe diameter.

7. The method for monitoring steam in veterinary drug production according to claim 1, characterized in that, It also includes steps for monitoring the recovery of steam condensate and assessing its thermal energy reuse: Monitor the condensate temperature and condensate flow rate at the outlet of each steam trap in the steam pipeline network; Calculate the total amount of residual heat energy carried by the condensate based on the condensate temperature and the condensate flow rate. The total amount of residual heat energy is matched with the heat energy required to supplement the current production process to find process nodes that can utilize the residual heat energy. If a matching process node exists, a condensate recovery path control instruction is generated, which is used to guide high-temperature condensate into the designated heat recovery device. The steam supply strategy is updated synchronously, and the original steam supply of the corresponding link is appropriately reduced while ensuring process requirements are met.

8. The method for monitoring steam in veterinary drug production according to claim 7, characterized in that, The total amount of residual heat energy is matched with the heat energy required for replenishment in the current production process to identify process nodes where the residual heat energy can be utilized, including: Obtain a list of thermal energy requirements for all currently running process nodes that require heating. The list includes the lower limit of the thermal energy requirement temperature, the total thermal energy requirement, and the thermal energy replenishment time window. Process nodes whose condensate temperature is higher than the lower limit of the required thermal energy temperature are selected to form a set of potential usable nodes; Calculate the thermal energy deficit of each node in the potentially available node set within the thermal energy replenishment time window; The total amount of residual heat energy is allocated proportionally to the set of potentially usable nodes, with the allocation priority determined based on a comprehensive score of heat energy deficit and temperature matching degree. To obtain the allocated process node, plan the condensate delivery pipeline route and calculate the original steam demand that the process node can reduce after allocating heat energy.

9. A veterinary drug production steam monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the veterinary drug production steam monitoring method as described in any one of claims 1 to 8.

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