Intelligent calculation method and medium for blast furnace cooling water heat load based on numerical integration

Abstract

The present application relates to a kind of blast furnace cooling water heat load intelligent calculation method and medium based on numerical integration, the method is initialized fixed parameter by computer program, real-time acquisition cooling water flow, water temperature difference, ambient temperature, cooling wall surface temperature and other multidimensional time series data, after abnormal value is eliminated and unit standardization conversion, instantaneous heat load is calculated point by point, time accumulation accurate calculation of total heat load in monitoring period is realized using trapezoidal numerical integration method, finally output core data and generate visual time series curve.The present application solves the technical problems that traditional static calculation method has large error, low degree of automation and poor dynamic adaptability, can be directly embedded into blast furnace DCS system, provides precise data support for furnace condition regulation and control, energy saving optimization and long-term operation of blast furnace, and has the advantages of high precision, strong automation, good versatility and outstanding practicality.

Description

Technical Field

[0001] This invention relates to the field of thermal monitoring technology for blast furnace ironmaking, specifically to a method for calculating the cumulative heat load and accurately calculating heat loss of a blast furnace cooling system based on a time-series numerical integration algorithm and implemented by a computer program. This method is applicable to dynamic monitoring of blast furnace conditions, energy-saving optimization, and quantitative assessment of heat loss. Background Technology

[0002] The blast furnace cooling system is the core system for ensuring the safe and stable operation of the blast furnace. Accurate calculation of its heat load is crucial for assessing hearth erosion, cooling wall damage, and optimizing the cooling regime. Current technologies often employ a static calculation method of "instantaneous cooling water flow rate × instantaneous water temperature difference," which only reflects the heat load at a specific moment and ignores the cumulative characteristics of blast furnace heat load fluctuations over time. This leads to large errors in the total heat load calculation and fails to provide accurate data support for blast furnace energy-saving control.

[0003] Furthermore, existing calculation methods lack standardized computer program implementation logic, relying heavily on manual processing of time-series data, which is inefficient and prone to introducing human error, making it difficult to meet the real-time requirements of blast furnace automated monitoring systems. Therefore, there is an urgent need for an automated calculation method based on numerical integration to achieve high-precision, dynamic calculation of blast furnace heat load and heat loss through computer programs. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent calculation method and medium for heat loss of blast furnace cooling water based on numerical integration. The method collects time-series data of cooling water flow rate and water temperature difference through a computer program, and uses the trapezoidal numerical integration method to calculate the heat load over time. This solves the problems of large errors, low automation, and inability to dynamically monitor traditional methods, thereby improving the accuracy of blast furnace thermal monitoring.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] A smart calculation method for blast furnace cooling water heat load based on numerical integration is proposed, which involves the following steps performed by a computer program:

[0007] S1. Data Initialization and Real-time Acquisition

[0008] The computer program initializes fixed parameters: cooling water density ρ = 1000 kg / m³ 3 The specific heat capacity of water is c = 4186 J / (kg·℃); real-time time-series data of the cooling system are collected through the blast furnace DCS system, including:

[0009] The timestamp array t = [t0, t1, ..., tn] represents the sampling step size. T = 1-5s;

[0010] The instantaneous flow rate array for cooling water is Flow=[Flow0,Flow1,...,Flown], with units of m³ / h;

[0011] Cooling wall inlet and outlet water temperature difference array T=[ T0, T1,..., Tn], in °C.

[0012] S2. Data Preprocessing and Unit Conversion

[0013] The program performs outlier filtering on the collected flow rate and temperature difference data (removing sudden data with deviations exceeding ±10%), and converts the flow rate unit from m³ / h to m³ / s using the following formula:

[0014] Flow_m³s(i) = Flow(i) / 3600

[0015] Where Flow_m³s(i) is the instantaneous flow rate (m³ / s) at the i-th time point, and Flow(i) is the original instantaneous flow rate (m³ / h).

[0016] S3. Instantaneous heat load calculation

[0017] The program calculates the instantaneous heat load Q(i) at each time point, using the following formula:

[0018] Q(i) = ρ × c × Flow_m³s(i) × T(i)

[0019] Where Q(i) is the instantaneous heat load (W) at the i-th time point, ρ is the density of cooling water, c is the specific heat capacity of water, and Flow_m³s(i) is the instantaneous flow rate after conversion. T(i) represents the water temperature difference at the i-th time point.

[0020] S4. Numerical integration calculation of total heat load

[0021] The instantaneous heat load time series data is integrated over time using an integral method (such as the trapezoidal numerical integration method) to obtain the total heat load Q_total within the monitoring period. The integration formula is as follows:

[0022]

[0023] The program automatically calculates the total heat load by traversing the timestamp array and performing cumulative integration.

[0024] S5. Results Output and Visualization

[0025] The program outputs the total heat load and average heat load within the monitoring period, and automatically generates a time-series change curve of "time-instantaneous heat load-total heat load in interval" to complete the data visualization display.

[0026] In addition, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described thereon.

[0027] Due to the adoption of the above technical solution, the beneficial effects achieved by this invention are:

[0028] This invention automatically calculates continuous instantaneous heat load and time-period average heat load by discrete integration of time-series data on blast furnace cooling water flow rate and temperature difference. This eliminates data bias caused by manual sampling and calculation, accurately reflecting the heat balance of the blast furnace and providing quantitative support for precise control of the blast furnace thermal regime. Operators can make timely adjustments based on dynamic heat load changes, avoiding over- or under-heating caused by experience-based judgments, reducing ineffective fuel consumption, and lowering the overall fuel ratio of the blast furnace. Simultaneously, it can promptly identify disturbances such as slag shedding and localized abnormal heat loads, allowing for early intervention to stabilize furnace conditions and reduce fuel waste caused by large fluctuations in furnace conditions. It is particularly suitable for vanadium-titanium blast furnaces, helping to maintain stable slag and reduce additional fuel compensation input due to furnace wall heat dissipation. Furthermore, based on the heat load data output by this invention, benchmarking and optimization of ore blending and slag-making regimes can be carried out, providing a reliable data foundation for fuel consumption reduction process optimization. It also reduces the probability of abnormal conditions such as suspended material and material collapse, reducing additional fuel consumption during abnormal condition handling, achieving a reduction in energy consumption per ton of iron, demonstrating significant energy-saving and consumption-reducing economic benefits, and possessing extremely high application value. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the process flow of the method of the present invention;

[0030] Figure 2 The time-instantaneous heat load variation curve of the method of the present invention;

[0031] Figure 3 This is the time-interval total heat load variation curve of the method of the present invention. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to the embodiments.

[0033] A smart calculation method for blast furnace cooling water heat load based on numerical integration, such as Figure 1 The following steps are performed via a computer program:

[0034] S1. Data Initialization and Real-time Acquisition

[0035] The computer program initializes fixed parameters: cooling water density ρ = 1000 kg / m³ 3 The specific heat capacity of water is c = 4186 J / (kg·℃); real-time time-series data of the cooling system are collected through the blast furnace DCS system, including:

[0036] The timestamp array t = [t0, t1, ..., tn] represents the sampling step size. T = 1-5s;

[0037] The instantaneous flow rate array for cooling water is Flow=[Flow0,Flow1,...,Flown], with units of m³ / h;

[0038] Cooling wall inlet and outlet water temperature difference array T=[ T0, T1,..., Tn], in °C.

[0039] S2. Data Preprocessing and Unit Conversion

[0040] The program performs outlier filtering on the collected flow rate and temperature difference data (removing sudden data with deviations exceeding ±10%), and converts the flow rate unit from m³ / h to m³ / s using the following formula:

[0041] Flow_m³s(i) = Flow(i) / 3600

[0042] Where Flow_m³s(i) is the instantaneous flow rate (m³ / s) at the i-th time point, and Flow(i) is the original instantaneous flow rate (m³ / h).

[0043] S3. Instantaneous heat load calculation

[0044] The program calculates the instantaneous heat load Q(i) at each time point, using the following formula:

[0045] Q(i) = ρ × c × Flow_m³s(i) × T(i)

[0046] Where Q(i) is the instantaneous heat load (W) at the i-th time point, ρ is the density of cooling water, c is the specific heat capacity of water, and Flow_m³s(i) is the instantaneous flow rate after conversion. T(i) represents the water temperature difference at the i-th time point.

[0047] S4. Numerical integration calculation of total heat load

[0048] The instantaneous heat load time series data is integrated over time using an integral method (such as the trapezoidal numerical integration method) to obtain the total heat load Q_total within the monitoring period. The integration formula is as follows:

[0049]

[0050] The program automatically calculates the total heat load by traversing the timestamp array and performing cumulative integration.

[0051] S5. Results Output and Visualization

[0052] The program outputs the total heat load and average heat load within the monitoring period, and automatically generates a time-series variation curve of "time-instantaneous heat load-total heat load in interval", such as... Figure 2 , Figure 3 Complete the data visualization display.

[0053] Computer program code

[0054] plaintext

[0055] / / Initialize fixed parameters

[0056] ρ=1000; / / Density of cooling water, kg / m³

[0057] c=4186; / / Specific heat capacity of water, J / (kg·℃)

[0058] T = 3; / / Data acquisition step size, s

[0059] n = total number of data sets;

[0060] / / Collect time series data

[0061] t = [t0, t1, ..., tn]; / / Timestamp array

[0062] Flow=[Flow0, Flow1, ..., Flow n ]; / / Instantaneous flow rate array, m³ / h

[0063] T=[ T0, T1,..., [Tn] / / Water temperature difference array, ℃

[0064] / / Data preprocessing: outlier removal + unit conversion

[0065] for i from 0 to n:

[0066] If |Flow(i) - mean(Flow)| > 0.1 × mean(Flow) or | T(i) - mean ( T)| >0.1×mean( T):

[0067] Remove Flow(i) T(i); / / Outlier removal

[0068] Flow_m³s(i) = Flow(i) / 3600; / / Unit conversion

[0069] / / Instantaneous heat load calculation

[0070] Q = [];

[0071] for i from 0 to n:

[0072] Q(i) = ρ×c×Flow_m³s(i)× T(i);

[0073] / / Trapezoidal integral of total heat load

[0074] Q_total=0;

[0075] for i from 0 to n-1:

[0076] Q_total += (Q(i) + Q(i+1)) / 2 × T;

[0077] Q_total = Q_total / 1e9; / / Convert to GJ

[0078] / / Result Output

[0079] Output "Total heat load:", Q_total, "GJ";

[0080] Generate time-series variation curves for "time-instantaneous heat load" and "time-total heat load over time", such as... Figure 2 , Figure 3 .

[0081] In addition, the present invention discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0082] Example 1

[0083] With a certain 2500m 3 The entire cooling system of the blast furnace was the monitoring target, with a monitoring cycle of 1 hour (3600s), a data acquisition step of 3.6s, and a total of 1000 sets of time series data were collected.

[0084] 1. Data collection: The total cooling water flow of the blast furnace was stable at 4400~4800 m³ / h (average 4600 m³ / h), and the overall inlet and outlet water temperature difference of the cooling wall was 2.8~3.2℃ (average 3.0℃); the ambient temperature was 20℃, and the average surface temperature of the cooling wall was 165℃.

[0085] 2. Data processing: The system automatically removes one set of abnormal instantaneous flow fluctuation values ​​caused by valve fine-tuning and completes unit conversion.

[0086] 3. Instantaneous heat load calculation: Based on the average value calculation, the instantaneous heat load is stable at around 16.0 MW.

[0087] 4. Integral calculation: By eliminating instantaneous fluctuations through the trapezoidal numerical integration method, the total heat load Q_total = 57.6 GJ for 1 hour is calculated.

[0088] Example 2

[0089] This embodiment is for the above-mentioned oxygen-enriched production increase condition of a 2500m³ blast furnace, and the monitoring cycle is 2 hours (7200s).

[0090] 1. Data Acquisition: To match the increase in heat load, the cooling water flow rate was adjusted to 4800~5200 m³ / h, and the water temperature difference was increased to 3.5~4.0℃; the ambient temperature was 25℃, and the wall temperature was increased to 185℃.

[0091] 2. Data processing: The data remained stable throughout the process, with no abnormalities removed.

[0092] 3. Instantaneous heat load calculation: Instantaneous heat load range 21.5~23.0 MW.

[0093] 4. Integral calculation: Total cumulative heat load Q_total = 165.8 GJ in 2 hours.

[0094] Example 3

[0095] This embodiment monitors the heat load recovery process 4 hours after the blast furnace is shut down and then restarted, with a data acquisition step size of 5 seconds.

[0096] 1. Data collection: The initial flow rate was 4200 m³ / h, and the temperature difference was only 1.5℃; later, when the furnace condition recovered, the flow rate increased to 4600 m³ / h, and the temperature difference rose back to 3.0℃.

[0097] 2. Data processing: Remove the three sets of data with drastic fluctuations at the moment of wind re-entry.

[0098] 3. Instantaneous heat load calculation: The heat load gradually increases from 4.0 MW to 16.0 MW.

[0099] 4. Integral calculation: Total heat load during the 4-hour recovery period Q_total = 145.2 GJ.

[0100] Example 4

[0101] This embodiment focuses on the case of localized thickening of the furnace belly during the 8th year of blast furnace service, with a monitoring cycle of 1 hour.

[0102] 1. Data collection: The total water volume was maintained at 4600 m³ / h, but due to localized thickening of the crust, the heat exchange was poor, and the overall water temperature difference dropped to 2.2~2.6℃ (average 2.4℃).

[0103] 2. Data processing: Data is normal.

[0104] 3. Instantaneous heat load calculation: The average instantaneous heat load drops to 12.5 MW.

[0105] 4. Integral calculation: Total heat load Q_total = 45.1 GJ.

[0106] The above embodiments are only used to illustrate and not limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention without departing from the spirit and scope of the present invention. Any modifications or partial substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart calculation method for blast furnace cooling water heat load based on numerical integration, characterized in that: Includes the following steps: S1. Data Initialization and Real-time Acquisition: Initialize cooling water density ρ = 1000 kg / m³ 3 The specific heat capacity of water is c = 4186 J / (kg·℃). The timestamp array t = [t0, t1, ..., t] of the cooling system is collected in real time by the blast furnace DCS system. n The instantaneous flow rate array for cooling water is Flow=[Flow0,Flow1,...,Flow]. n ] Cooling wall inlet and outlet water temperature difference array T=[ T0, T1,..., T n The data acquisition step size is... t, the instantaneous flow rate array of cooling water (Flow) is in m³ / h, and the temperature difference array between the inlet and outlet water of the cooling wall is... The unit of T is °C; S2. Data Preprocessing and Unit Conversion: Outlier removal is performed on flow rate and temperature difference data, and the flow rate unit is converted from m³ to m³. 3 / h is converted to m 3 / s, the conversion formula is Flow_m³s(i)=Flow(i) / 3600; where Flow_m³s(i) is the instantaneous flow rate at the i-th time point, m³ / s; Flow(i) is the original instantaneous flow rate, in m³ / h; S3. Instantaneous heat load calculation: Calculate the instantaneous heat load at each time point, using the formula Q(i) = ρ × c × Flow_m³s(i) × T(i); where Q(i) is the instantaneous heat load at the i-th time point, in W; ρ is the density of cooling water, c is the specific heat capacity of water, and Flow_m³s(i) is the instantaneous flow rate after conversion. T(i) represents the water temperature difference at the i-th time point; S4. Numerical integration calculation of total heat load: The total heat load during the monitoring period is obtained by integrating the instantaneous heat load time series data using the integration method: ； S5. Results Output and Visualization: Outputs the total heat load, average heat load, total heat loss and heat loss rate within the monitoring period, and automatically generates a time-series change curve of "time-instantaneous heat load-total heat load in interval" to complete the data visualization display.

2. The intelligent calculation method for blast furnace cooling water heat load based on numerical integration according to claim 1, characterized in that: In step S1, the data acquisition step size t is 1~5s, timestamp array t, instantaneous cooling water flow rate array Flow, and cooling wall inlet and outlet water temperature difference array. T represents the time-series data acquired synchronously.

3. The intelligent calculation method for blast furnace cooling water heat load based on numerical integration according to claim 1, characterized in that: In step S2, outlier removal employs a dual verification method of the 3σ criterion and the mean deviation method to ensure data validity.

4. The intelligent calculation method for blast furnace cooling water heat load based on numerical integration according to claim 3, characterized in that: In step S2, mutation data with a deviation exceeding ±10% of the mean are removed.

5. The intelligent calculation method for blast furnace cooling water heat load based on numerical integration according to claim 1, characterized in that: In step S4, the integration method is the trapezoidal numerical integration method.

6. The intelligent calculation method for blast furnace cooling water heat load based on numerical integration according to claim 1, characterized in that: In step S5, the monitoring cycle is 1 hour to 4 hours.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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