A method for controlling temperature rise in sulfuric acid conversion device

Abstract

The present invention relates to the technical field of sulfuric acid conversion temperature rise, and is about a sulfuric acid device conversion temperature rise control method, the method comprising: dividing the electric furnace temperature rise process into a plurality of sub-periods, keeping the duration of each sub-period equal, and monitoring the electric furnace temperature rise process in different sub-periods in real time; in each sub-period, according to the performance requirements of the vanadium catalyst, setting the power output standard of the electric furnace, monitoring the power output of the electric furnace in real time through the DCS system, and performing abnormal analysis and processing on the power output. The present invention places the power output after abnormal analysis and processing and the deviation between the actual temperature of the catalytic reaction zone and the set temperature in the same sub-period for comprehensive analysis, performs intelligent perception of the temperature control anomaly in the electric furnace temperature rise process, and promptly processes the situation, thereby reducing the decrease in the activity of the vanadium catalyst caused by improper temperature control, improving production efficiency, and extending the service life of the vanadium catalyst.

Description

Technical Field

[0001] The invention relates to the technical field of conversion temperature rise of a sulfuric acid device, and in particular to a conversion temperature rise control method of a sulfuric acid device. Background Art

[0002] At present, the heating process and equipment of vanadium catalyst conversion in large-scale sulfuric acid plants in China basically adopt foreign sulfuric acid production technology, using the high-temperature gas from the combustion of diesel in the preheating furnace to indirect heat exchange with dry cold air to heat up the vanadium catalyst conversion. Imported vanadium catalyst conversion is used in the design. The ignition temperature of imported vanadium catalyst conversion is lower than that of domestic vanadium catalyst conversion, and the performance is excellent. The heat required for conversion heating is less. Therefore, when large-scale sulfuric acid plants are equipped with imported vanadium catalyst conversion, diesel heating can be used normally. Another reason for the use of diesel in large-scale sulfuric acid plants is that large-scale sulfuric acid plants use electric furnaces for heating, and the power load demand is large. A 400kt / a sulfuric acid plant requires an electricity load of about 5000kw / h, and the investment in the power supply and distribution system is large. In addition, the power supply capacity of the location of the plant must also be considered. Therefore, while large-scale sulfuric acid plants in China use foreign sulfuric acid production technology, in order to save investment costs, they generally use domestic vanadium catalyst conversion, which can meet the operating conditions during normal production, but generally encounter problems such as difficulty in heating and excessive tail gas from feeding during conversion heating.

[0003] The converter of the existing sulfuric acid plant uses vanadium catalyst to convert the SO2 furnace gas sent by the main fan into SO3 under heating and catalysis of the vanadium catalyst. At the same time, there is a phenomenon in the use of vanadium catalyst in sulfuric acid plants. Whenever the shutdown time exceeds 24 hours, the vanadium catalyst must be heated up. Especially after a long shutdown for annual inspection, the heating time of the vanadium catalyst will be longer. Therefore, how to smoothly and quickly heat up the vanadium catalyst effectively is an urgent problem to be solved in the existing sulfuric acid plants, and it is a research topic for sulfuric acid enterprises to reduce energy consumption and improve the economic benefits of the equipment.

[0004] In order to improve the above problems, the existing technology changes the original design of the second-turn vanadium catalyst heating, and innovatively uses it to automatically control the heating of the electric furnace. After applying the new technology, the second-turn heating only needs to start the electric furnace heating 8 hours before the system is ventilated. The number of electric furnaces to be put into use is determined according to the needs of the conversion heating. The relevant control of the electric furnace is incorporated into the main control DCS system to achieve remote automatic control. Through this improved method, the heating speed of the electric furnace is fast and uniform, and the activation temperature of the vanadium catalyst can be reached in a short time, which greatly shortens the preparation time before startup; the energy efficiency of the electric furnace is usually higher than that of the traditional fuel combustion heating method, and more energy is directly converted into heat energy, reducing energy loss. The electric furnace can accurately control the heating temperature and rate to avoid overheating and energy waste, which is more economical than the combustion of fuels such as diesel; the use of electric furnace heating avoids pollutants generated when burning fuels, such as SOx, NOx and particulate matter, which is conducive to meeting increasingly stringent environmental protection requirements. Electricity can be obtained through renewable energy, further reducing the carbon footprint of the entire process.

[0005] However, the prior art has the following deficiencies:

[0006] During the heating process of the electric furnace, if the temperature is improperly controlled and the temperature inside the electric furnace is too high or too low, the high temperature may cause the vanadium component in the vanadium catalyst structure to sinter, reduce the surface area, and reduce the effective catalytic active points. It may also cause the volatilization or recombination of chemical species on the surface of the vanadium catalyst, changing its catalytic properties, thereby reducing the SO2 conversion efficiency. Too low a temperature will cause the vanadium catalyst to fail to reach an activated state, resulting in a low conversion rate from SO2 to SO3, reducing production efficiency and increasing production costs. Secondly, the time required for the vanadium catalyst to reach a working state at low temperatures will increase, which will also lead to delays in production startup.

[0007] Therefore, there is an urgent need to accurately monitor and analyze the heating process of the electric furnace, intelligently sense improper temperature control, and make timely adjustments to the situation to effectively avoid the occurrence of the above-mentioned problems. Summary of the invention

[0008] The purpose of the present invention is to provide a method for controlling the temperature rise of a sulfuric acid device during conversion. The power output of an electric furnace is monitored in real time by a DCS system, and abnormal analysis and processing of the power output are performed. Then, the actual heating rate of a vanadium catalyst in a catalytic reaction zone is obtained, and the actual heating rate is compared with the expected heating rate to calculate the heating deviation. The power output after the abnormal analysis and processing and the actual temperature of the catalytic reaction zone and the set temperature deviation are placed in the same sub-period for comprehensive analysis. The temperature control abnormality in the heating process of the electric furnace is intelligently sensed, and the situation is promptly processed to reduce the decrease in the activity of the vanadium catalyst caused by improper temperature control, improve production efficiency, and extend the service life of the vanadium catalyst to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention provides a method for controlling the temperature rise of a sulfuric acid device during conversion, comprising the following steps:

[0010] Step 1: Divide the heating process of the electric furnace into several sub-periods, keep the duration of each sub-period equal, and monitor the heating process of the electric furnace in different sub-periods in real time;

[0011] Step 2: In each sub-period, according to the performance requirements of the vanadium catalyst, the power output standard of the electric furnace is set, the power output of the electric furnace is monitored in real time through the DCS system, and the power output is analyzed and processed abnormally to obtain the power output floating index;

[0012] Step 3: Install a temperature sensor close to the surface of the vanadium catalyst, obtain the actual heating rate of the vanadium catalyst in the catalytic reaction zone in each sub-period, and compare it with the expected heating rate to calculate the heating deviation, and obtain the heating rate control deviation index;

[0013] Step 4: Place the power output floating index and the actual heating speed in the same sub-period, and place the set temperature deviation in the same sub-period for comprehensive analysis, establish a temperature control abnormality perception model for comprehensive analysis, generate a temperature control abnormality evaluation coefficient, and perform intelligent evaluation through the temperature control abnormality in the electric furnace heating process;

[0014] Step 5: If there is temperature control abnormality during the heating process of the electric furnace, the heating process of the electric furnace visualized by the DCS system is used to perform difference analysis between the actual heating curve drawn and the predetermined heating target curve to confirm the abnormality of the heating process of the electric furnace and obtain the difference index;

[0015] Step 6: Based on the difference index, the heating process of the electric furnace is timely regulated and controlled, and the regulation process is monitored in real time to ensure successful regulation of the heating process of the electric furnace.

[0016] In one implementation method, the abnormal analysis process includes:

[0017] The power output of the electric furnace is obtained from the power metering device of the electric furnace, and the power output of the electric furnace is represented by a function P(t) according to a time series, where t represents time;

[0018] Divide each sub-period into n sub-periods of equal length, where n is a positive integer;

[0019] Calculate the average of the average power outputs of all sub-periods within the entire sub-period to determine the standard power level for the entire sub-period:

[0020] represents the average power output of the electric furnace in the entire sub-period. represents the average power output of the electric furnace during the sub-period;

[0021] Calculate the power output floating index, the power output floating index P out It is the maximum absolute value of the difference between the average power of all sub-periods and the average power of the entire sub-period, reflecting the maximum fluctuation of power output. The calculation expression is:

[0022] In one implementation method, calculating the average of the average power outputs of all sub-sub-periods in the entire sub-period includes:

[0023] The average power output expression is:

[0024]

[0025] The duration of each sub-period is Δt. For each sub-period; t k =□Δt, indicating the start time of the kth sub-period, It represents the average power output of the electric furnace in the sub-period.

[0026] In one implementation method, the actual heating rate of the vanadium catalyst in the catalytic reaction zone is obtained and compared with the expected heating rate to calculate the temperature deviation, comprising:

[0027] In each sub-period, the temperature change of the vanadium catalyst is directly captured by a temperature sensor installed in the catalytic reaction zone, and the temperature output of the sensor is represented by the function Tem(t) according to the time series;

[0028] Using continuous temperature readings, the actual heating rate is calculated using a differential formula;

[0029] At any time t, the heating rate deviation is calculated from the difference between the actual heating rate and the expected heating rate. The calculation expression is: d(t) = r actual (t)-r expected , where d(t) represents the deviation between the actual heating rate and the expected heating rate at time t, r actual (t) represents the actual heating rate at time t, r expected represents the expected heating rate at time t;

[0030] According to the expression: The heating speed control deviation index is calculated, where HR di represents the heating speed control deviation index, M represents the total duration of the sub-period, and t1 and t2 are the starting point and end point of the sub-period, respectively.

[0031] In one implementation, the difference formula is:

[0032]

[0033] Among them, r actual (t) represents the actual heating rate at time t, and Δt is the time interval between two adjacent temperature readings.

[0034] In one implementation method, after generating the temperature control abnormality assessment coefficient, the method includes:

[0035] The temperature control abnormality assessment coefficient is compared and analyzed with the preset temperature control abnormality assessment coefficient reference threshold value.

[0036] If the temperature control abnormality assessment coefficient is greater than or equal to the temperature control abnormality assessment coefficient reference threshold, a temperature control hidden danger signal is issued;

[0037] If the temperature control abnormality assessment coefficient is less than the temperature control abnormality assessment coefficient reference threshold, a temperature control high efficiency signal is issued.

[0038] In one implementation method, after the temperature control hidden danger signal is issued, the method further includes:

[0039] The temperature control anomaly assessment coefficients generated in the subsequent several sub-periods are obtained to establish an analysis set for comprehensive analysis. The comprehensive analysis process includes the following steps:

[0040] By analyzing the temperature control anomaly assessment coefficients in the set, the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are calculated, and the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are compared and analyzed with the pre-set temperature control anomaly assessment coefficient reference threshold and standard deviation reference threshold respectively;

[0041] If the mean value of the temperature control abnormality assessment coefficient is less than the reference threshold value of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold value of the standard deviation, a sudden signal is issued, indicating that there is a sudden temperature control abnormality risk in the heating process of the electric furnace;

[0042] If the mean of the temperature control abnormality assessment coefficient is less than the reference threshold of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold of the standard deviation, a risk signal is issued, indicating that there is no hidden danger of sudden temperature control abnormality in the heating process of the electric furnace.

[0043] In one implementation, the differential analysis includes:

[0044] Derive the actual temperature curve data T of the electric furnace from the DCS system ac (t) and the predetermined target temperature curve data T arge (t), where t represents the measurement time point;

[0045] Align the time points to ensure that the two sets of data T ac (t) and T arge (t) comparisons are made at the same time intervals and lengths;

[0046] The actual temperature data T ac (t) and target temperature data T arge (t) is regarded as two vectors, expressed as and

[0047] According to the cosine similarity formula, we can calculate and Cosine similarity Cos sim ;

[0048] Based on the expression: θ diff =1-Cos sim The difference index is calculated, where θ diff Represents the difference index.

[0049] In one implementation method, timely regulating the heating process of the electric furnace based on the difference index includes:

[0050] Comparing and analyzing the difference index with a preset difference index reference threshold;

[0051] If the difference index is less than the difference index reference threshold, a control success signal is generated, indicating that the control is successful;

[0052] If the difference index is greater than or equal to the difference index reference threshold, a control failure signal is generated, indicating that the control has failed and that control needs to be continued.

[0053] In one implementation method, the cosine similarity formula is:

[0054]

[0055] Among them, the dot product Cos sim express and The cosine similarity of .

[0056] The technical solution provided by this application may have the following beneficial effects:

[0057] This technical solution monitors the power output of the electric furnace in real time through the DCS system, and performs abnormal analysis and processing on the power output, then obtains the actual heating rate of the vanadium catalyst in the catalytic reaction zone, and compares it with the expected heating rate to calculate the heating deviation. The power output after abnormal analysis and processing and the actual temperature of the catalytic reaction zone and the set temperature deviation are placed in the same sub-period for comprehensive analysis, and the temperature control anomalies in the heating process of the electric furnace are intelligently sensed and handled in a timely manner, thereby reducing the decrease in the activity of the vanadium catalyst caused by improper temperature control, improving production efficiency and extending the service life of the vanadium catalyst.

[0058] When the present invention senses that there is a temperature control abnormality in the heating process of an electric furnace, the heating process of the electric furnace visualized by the DCS system is used to perform a difference analysis between the drawn actual heating curve and the predetermined heating target curve to generate a difference index. When the heating process of the electric furnace is regulated, the difference index generated during the abnormal analysis of the heating process of the electric furnace is used as a reference to ensure successful regulation of the heating process of the electric furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0060] Figure 1 This is a schematic flow chart of a method for heating up a sulfuric acid plant through conversion according to an embodiment of the present invention;

[0061] Figure 2 A schematic diagram of a conversion catalyst heating process corresponding to an existing sulfuric acid device according to an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of the conversion catalyst heating process corresponding to the improved sulfuric acid device shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0064] Example

[0065] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0066] The present invention provides Figure 1The conversion temperature control method of the sulfuric acid device shown includes the following steps:

[0067] Step 1, dividing the heating process of the electric furnace into several sub-periods, making the duration of each sub-period equal, and monitoring the heating process of the electric furnace in different sub-periods in real time;

[0068] The heating process of the electric furnace is divided into several sub-periods of equal length so that the heating effect of each period can be monitored in real time. This segmented monitoring strategy can not only ensure that the vanadium catalyst is heated efficiently, but also help to timely detect and adjust the situation of excessively high or low temperature, thereby optimizing the performance of the catalyst and extending its service life.

[0069] Step 2: In each sub-period, according to the performance requirements of the vanadium catalyst, the power output standard of the electric furnace is set, the power output of the electric furnace is monitored in real time through the DCS system, and abnormal analysis and processing of the power output is performed;

[0070] Vanadium catalysts are commonly used to promote oxidation reactions in sulfuric acid production, converting SO2 into SO3. The performance of vanadium catalysts is affected by heating temperature and environmental conditions, so the temperature of the electric furnace must be precisely controlled, which is directly related to the power output of the electric furnace. Setting the power output standard means determining the energy output level that the electric furnace should have under normal operating conditions, which is usually measured in kilowatts (kW). Using a distributed control system (DCS) to monitor the power output of the electric furnace in real time is to ensure that the electric furnace can operate stably according to the preset standards.

[0071] During the vanadium catalyst heating process of the electric furnace, if the power output fluctuates greatly, it may indeed lead to improper temperature control of the electric furnace, thus causing the problem of high or low temperature. The following is a detailed explanation of why the unstable power output will affect temperature control:

[0072] The power output of an electric furnace determines how much heat it generates and is the main factor that directly affects the temperature of the furnace. Ideally, the power output should be precisely matched to the heat demand of the furnace to maintain the set temperature. If the power output fluctuates greatly, even if the power is high or low, it will directly cause the temperature inside the furnace to fluctuate and fail to maintain the predetermined stable level.

[0073] High power output: too much heat is generated in a short period of time, the temperature of the electric furnace rises rapidly, exceeding the safe or optimal operating temperature of the vanadium catalyst, increasing the risk of catalyst sintering or thermal degradation.

[0074] Low power output: Insufficient heat generation, the furnace temperature is lower than the required level, causing the vanadium catalyst to fail to reach an effective reaction temperature, affecting the efficiency and integrity of the chemical reaction.

[0075] The DCS system monitors the power output of the electric furnace in real time, performs abnormal analysis on the power output, and generates a power output floating index. The logic for obtaining the power output floating index is:

[0076] The power output of the electric furnace is obtained from the power metering device of the electric furnace, and the power output of the electric furnace is represented by a function P(t) according to a time series, where t represents time;

[0077] Each sub-period is divided into n equal-length sub-periods, where n is a positive integer and the duration of each sub-period is Δt. For each sub-period, calculate its average power output: Among them, t k =□Δt, indicating the start time of the kth sub-period, represents the average power output of the electric furnace during the sub-period;

[0078] Calculate the average of the average power outputs of all sub-periods within the entire sub-period to determine the standard power level for the entire sub-period:

[0079]

[0080] represents the average power output of the electric furnace in the entire sub-period. represents the average power output of the electric furnace during the sub-period;

[0081] Calculate the power output floating index, power output floating index P out It is defined as the maximum absolute value of the difference between the average power of all sub-periods and the average power of the entire sub-period, reflecting the maximum fluctuation of power output. The calculation expression is:

[0082] The power output floating index reveals the stability of power output in different electric heating periods. The larger the value, the greater the power output fluctuation, indicating that the electric furnace has a greater risk of abnormal temperature control during the heating of the vanadium catalyst. Conversely, the larger the value, the smaller the power output fluctuation, indicating that the electric furnace has a smaller risk of abnormal temperature control during the heating of the vanadium catalyst.

[0083] Install a temperature sensor close to the surface of the vanadium catalyst, obtain the actual heating rate of the vanadium catalyst in the catalytic reaction zone in each sub-period, and compare it with the expected heating rate to calculate the heating deviation;

[0084] During the heating process of the electric furnace, when the vanadium catalyst is heated, the deviation between the actual heating rate of the vanadium catalyst in the catalytic reaction zone and the expected heating rate will indeed lead to improper temperature control of the electric furnace. This is because the temperature control of the electric furnace depends on the accurate control of the heating rate of the vanadium catalyst to ensure a stable temperature in the catalytic reaction zone. If the heating rate is too low, the temperature of the electric furnace may rise too slowly to reach the required temperature, resulting in a low temperature; on the contrary, if the heating rate is too high, it may cause the temperature to rise too quickly and be difficult to control, resulting in a high temperature. Therefore, ensuring that the heating rate is consistent with expectations is crucial to effectively control the temperature of the electric furnace.

[0085] The actual heating rate of the vanadium catalyst in the catalytic reaction zone is obtained and compared with the expected heating rate to calculate the temperature deviation. The heating rate control deviation index is generated by the speed deviation. The acquisition logic of the heating rate control deviation index is:

[0086] Step 3, in each sub-period, directly capture the temperature change of the vanadium catalyst through a temperature sensor installed in the catalytic reaction zone, and express the temperature output of the sensor by a function Temm(t) according to the time series;

[0087] Using the continuous temperature readings, the actual heating rate is calculated using the differential formula: Among them, r actual (t) represents the actual heating rate at time t, and Δt is the time interval between two adjacent temperature readings;

[0088] At any time t, the heating rate deviation is calculated from the difference between the actual heating rate and the expected heating rate. The calculation expression is: d(t) = r actual (t)-r expected , where d(t) represents the deviation between the actual heating rate and the expected heating rate at time t;

[0089] Calculate the heating speed control deviation index, the calculation expression is: In the formula, HR di represents the heating speed control deviation index, M represents the total duration of the sub-period, and t1 and t2 are the starting point and end point of the sub-period, respectively.

[0090] The smaller the value of the heating speed control deviation index is, the closer the heating speed is to the expected value and the more precise the control of the heating process is, reflecting that the system has a strong control ability over temperature fluctuations and can effectively ensure the efficiency and safety of the chemical reaction. It indicates that the risk of abnormal temperature control in the electric furnace during the heating of the vanadium catalyst is greater. Conversely, the risk of abnormal temperature control in the electric furnace during the heating of the vanadium catalyst is smaller.

[0091] Step 4, the power output after abnormal analysis and the deviation between the actual temperature of the catalytic reaction zone and the set temperature are placed in the same sub-period for comprehensive analysis, and the temperature control abnormality existing in the heating process of the electric furnace is intelligently evaluated;

[0092] Get the power output floating index P generated in each sub-period out and heating rate control deviation index HR di After that, the power output floating index P out and heating rate control deviation index HR di In the same sub-period, a temperature control anomaly perception model is established for comprehensive analysis to generate a temperature control anomaly evaluation coefficient Contol τ , intelligent evaluation is carried out through temperature control anomalies that exist during the heating process of the electric furnace.

[0093] Step 5: The temperature control abnormality perception model is not specifically limited here, and can realize the power output floating index P out and heating rate control deviation index HR di Perform comprehensive analysis and generate temperature control abnormality evaluation coefficient Contol τ In order to realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0094] Temperature control abnormality evaluation coefficient Contol τ The generated calculation formula is: Contol τ =α*I τ +β*r μ , where α and β are the power output floating index P out and heating rate control deviation index HR di The preset proportional coefficient, and α and β are both greater than 0.

[0095] It can be seen from the calculation formula that the larger the power output floating index generated in each sub-period and the larger the heating speed control deviation index, that is, the larger the performance value of the generated temperature control abnormality evaluation coefficient, the greater the hidden danger of temperature control abnormality in the electric furnace during the heating of the vanadium catalyst. Conversely, the smaller the hidden danger of temperature control abnormality in the electric furnace during the heating of the vanadium catalyst.

[0096] The temperature control abnormality assessment coefficient generated in each sub-period is compared and analyzed with the pre-set temperature control abnormality assessment coefficient reference threshold. If the temperature control abnormality assessment coefficient is greater than or equal to the temperature control abnormality assessment coefficient reference threshold, a temperature control hidden danger signal is generated, indicating that there is a hidden danger of temperature control abnormality in the electric furnace heating process. If the temperature control abnormality assessment coefficient is less than the temperature control abnormality assessment coefficient reference threshold, a temperature control high-efficiency signal is generated, indicating that the electric furnace heating process can be carried out stably and efficiently.

[0097] Example

[0098] When the temperature control hidden danger signal is generated during the heating process of the electric furnace, the temperature control abnormality assessment coefficients generated in the subsequent sub-periods are obtained to establish an analysis set for comprehensive analysis. The comprehensive analysis process is as follows:

[0099] By analyzing the temperature control anomaly assessment coefficients in the set, the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are calculated, and the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are compared and analyzed with the pre-set reference threshold value of the temperature control anomaly assessment coefficient and the standard deviation reference threshold value respectively;

[0100] If the mean value of the temperature control abnormality assessment coefficient is less than the reference threshold value of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold value of the standard deviation, a burst signal is generated, indicating that there is a sudden temperature control abnormality hidden danger in the heating process of the electric furnace. This sudden temperature control abnormality hidden danger has little impact on the heating process of the electric furnace, which also indicates that the heating process of the electric furnace can be carried out stably and efficiently.

[0101] If the mean of the temperature control abnormality assessment coefficient is less than the reference threshold of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold of the standard deviation, a risk signal is generated, indicating that there is no sudden temperature control abnormality risk in the heating process of the electric furnace. At this time, the temperature control abnormality risk has a greater impact on the heating process of the electric furnace, indicating that there is a temperature control abnormality in the heating process of the electric furnace.

[0102] If there is temperature control abnormality during the heating process of the electric furnace, the heating process of the electric furnace visualized by the DCS system is used to analyze the difference between the actual heating curve drawn and the predetermined heating target curve to confirm the abnormality of the heating process of the electric furnace;

[0103] If the electric furnace heating process generates a risk signal, the electric furnace heating process visualized by the DCS system is used to generate a difference index by performing a difference analysis between the actual heating curve and the predetermined heating target curve;

[0104] The logic for obtaining the difference index is:

[0105] Derive the actual temperature curve data T of the electric furnace from the DCS system ac (t) and the predetermined target temperature curve data T arge (t), where t represents the measurement time point;

[0106] Align the time points to ensure that the two sets of data T ac (t) and T arge (t) comparisons are made at the same time intervals and lengths;

[0107] The actual temperature data T ac (t) and target temperature data T arge (t) is regarded as two vectors, expressed as and

[0108] Calculated by cosine similarity formula and The cosine similarity is calculated as: Among them, the dot product Cos sim express and The cosine similarity of

[0109] and The closer the cosine similarity value is to 1, the better the actual temperature curve data T ac (t) and target temperature curve data T arge (t)The more similar, and The closer the cosine similarity value is to 0, the better the actual temperature curve data T ac (t) and target temperature curve data T arge (t)The more dissimilar.

[0110] Calculate the difference index, the calculation expression is: θ diff =1-Cos sim , where θ diff Represents the difference index.

[0111] From the calculation expression of the difference index, we can know that the actual temperature curve data T ac (t) and target temperature curve data T arge (t) The difference index θ generated between diff The larger the performance value, the greater the deviation between the actual temperature control of the electric furnace and the target temperature. Conversely, the smaller the deviation between the actual temperature control of the electric furnace and the target temperature.

[0112] Step 6: Based on the abnormal analysis results of the electric furnace heating process, timely control the electric furnace heating process, and monitor the control process in real time to ensure that the electric furnace heating process is successfully controlled;

[0113] Based on the difference index generated during the abnormal analysis of the electric furnace heating process, the electric furnace heating process is regulated in time to ensure the successful regulation of the electric furnace heating process. The specific implementation process is as follows:

[0114] When the heating process of the electric furnace is regulated, the difference index generated by the regulation process is obtained, and the difference index is compared and analyzed with the pre-set difference index reference threshold. If the difference index is less than the difference index reference threshold, a regulation success signal is generated, indicating that the regulation is successful. If the difference index is greater than or equal to the difference index reference threshold, a regulation failure signal is generated, indicating that the regulation has failed and that regulation needs to continue.

[0115] If the temperature control is abnormal due to large fluctuations in power output during the heating process of the electric furnace, the following two existing technical means can be used to intelligently control the electric furnace to improve and optimize the temperature control accuracy and stability:

[0116] Adopt advanced PID control algorithm combined with adaptive control technology

[0117] The first step of intelligent control is to adopt advanced proportional-integral-derivative (PID) control algorithm. PID controller is a feedback loop control technology widely used in industry. It dynamically adjusts the control output through the set proportional (P), integral (I) and differential (D) parameters to reduce errors and quickly reach the target state.

[0118] Adaptive control enhancement: In order to deal with the floating problem of power output, the PID controller can be combined with adaptive control technology. Adaptive control can automatically adjust the PID parameters according to the changes in actual operating conditions (such as power fluctuations). This method can dynamically optimize the control parameters to cope with different working environments and conditions, thereby more accurately controlling the heating process of the electric furnace.

[0119] Implementation method: Necessary sensors are installed to monitor the temperature inside the electric furnace and the external input power in real time. These data will be fed back to a central control unit (such as a PLC or DCS system). The system uses an adaptive PID algorithm to adjust the output power in real time to stabilize the temperature to the target set value, thereby reducing the temperature instability problem caused by power fluctuations.

[0120] Example

[0121] Optimizing Temperature Control Process Using Fuzzy Logic Controller

[0122] Fuzzy logic control is a control system based on fuzzy logic set theory. It does not require an accurate mathematical model, but relies on fuzzy sets and reasoning to deal with uncertainty and precision, which makes it particularly suitable for dealing with complex systems under the influence of unstable factors such as power fluctuations.

[0123] Application of fuzzy control: In electrical control systems, fuzzy logic controllers can be designed to handle input power fluctuations. By setting fuzzy rules (such as increasing power output when the temperature is low and the power is unstable), the control system can maintain the stability and accuracy of temperature control when facing large fluctuations in input power.

[0124] Implementation method: First, appropriate fuzzy control rules and membership functions are set according to the operation requirements and environmental conditions of the electric furnace. These rules and functions are then used to process real-time data (temperature readings and power changes). The fuzzy controller evaluates the current situation and adjusts the power output to maintain the temperature stability within a predetermined range. This control method is not only sensitive to small changes in parameters, but also can effectively suppress overshoot or oscillation caused by large fluctuations in parameters, optimizing the entire heating process.

[0125] If there is a deviation between the actual heating rate of the vanadium catalyst in the catalytic reaction zone and the expected heating rate during the heating process of the electric furnace, especially when this deviation is caused by the power output fluctuation of the electric furnace, the existing technology provides a variety of methods for intelligent regulation to improve the accuracy and stability of temperature control. The following are two technical application solutions for this problem:

[0126] Implementing advanced PID control strategies

[0127] The use of advanced PID (proportional-integral-differential) control strategy can effectively deal with the fluctuation of power output and the inconsistency of heating speed. The PID controller dynamically adjusts the heating power by adjusting its three parameters: proportional (P), integral (I), and differential (D) to reduce the deviation between the actual temperature and the target temperature.

[0128] Dynamic adjustment: By adding a derivative control portion, the PID controller can react to rapid changes in power, thereby counteracting temperature jumps caused by power fluctuations. Derivative control focuses on predicting future deviations, so it is able to adjust power output in advance to reduce the impact of deviations.

[0129] Adaptive Adjustment: In some more advanced applications, adaptive PID control can be implemented to automatically adjust the PID parameters based on past performance to suit current heating conditions. This control method is particularly suitable for dealing with unstable power output during the heating process of electric furnaces, ensuring that the heating rate is as close to the preset target as possible.

[0130] Using Model Predictive Control (MPC)

[0131] Model Predictive Control (MPC) is an advanced process control technology that uses past and current data to predict future process variables and optimize current control inputs. In electric furnace applications, MPC can adjust power output in real time to accommodate heating rate requirements and reduce temperature control problems caused by power fluctuations.

[0132] Prediction and optimization: MPC predicts future temperature changes based on a set model and calculates the optimal power output setting to minimize the total deviation during the prediction period. This method can dynamically adjust the power according to power fluctuations, making the actual heating speed closer to expectations, thereby improving the accuracy of temperature control.

[0133] Handling Complex Variables: MPC’s ability to handle multiple input and output variables simultaneously makes it particularly suitable for systems such as electric furnaces where temperature and power output are highly correlated and affected by many factors. MPC can stabilize temperature throughout the heating cycle, maintaining heating performance even when external conditions and internal reaction conditions change.

[0134] The present invention monitors the power output of the electric furnace in real time through a DCS system, performs abnormal analysis on the power output, obtains the actual heating rate of the vanadium catalyst in the catalytic reaction zone, and compares it with the expected heating rate to calculate the heating deviation. The power output after the abnormal analysis and the deviation between the actual temperature of the catalytic reaction zone and the set temperature are placed in the same sub-period for comprehensive analysis. The temperature control abnormality in the heating process of the electric furnace is intelligently sensed and the situation is promptly processed, thereby reducing the decrease in the activity of the vanadium catalyst caused by improper temperature control, improving production efficiency and extending the service life of the vanadium catalyst.

[0135] When the present invention senses that there is a temperature control abnormality in the heating process of an electric furnace, the heating process of the electric furnace visualized by the DCS system is used to perform a difference analysis between the drawn actual heating curve and the predetermined heating target curve to generate a difference index. When the heating process of the electric furnace is regulated, the difference index generated during the abnormal analysis of the heating process of the electric furnace is used as a reference to ensure successful regulation of the heating process of the electric furnace.

[0136] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0137] Example

[0138] In actual application, in order to improve the utilization rate of the conversion catalyst, the following operations can be performed in this example:

[0139] For example, see Figure 3 and Figure 2 , Figure 2 This is a schematic diagram of the conversion catalyst heating process corresponding to the existing sulfuric acid device. Figure 3 This is a schematic diagram of the conversion catalyst heating process corresponding to the improved sulfuric acid unit; the original conversion heating process is:

[0140] The flue gas from the main fan is heated to 400-450℃ by indirect heat exchange in the preheater, and then the flue gas is divided into two paths for circulation, one part of the flue gas is transferred to the first, second and third sections for heating and heating respectively, and the remaining part is transferred to the fourth and fifth sections for heating respectively, and finally all the converted flue gas is transferred to an absorption tower at one time for cooling, the temperature is reduced to about 70℃, and then the flue gas in the first absorption tower is transferred to the fourth and fifth sections for secondary conversion; and, due to the large volume and low temperature of this part of gas, it is difficult for the fourth and fifth sections to reach the requirement of 380-400℃, which affects the quality of the produced sulfuric acid; therefore, the existing sulfuric acid device has the following problems during conversion and heating:

[0141] (1) The environmental risk is high in the initial stage of feeding. The catalyst temperature of the first, second and third stages of the conversion can meet the requirements, but the catalyst temperature index of the fourth and fifth stages of the conversion cannot meet the requirements, resulting in a low overall conversion rate of the entire system and a high sulfur dioxide emission concentration, which poses a great environmental risk.

[0142] (2) When the conversion catalyst is heated up, the preheater is used for a long time and the diesel consumption is large. In addition, due to the low temperature and poor activity of the catalyst, the sulfuric acid unit must control the furnace gas concentration within a very low concentration range to ensure the system conversion rate and sulfur dioxide emissions meet the standards. Therefore, the preheater needs to be used for a long time to assist in heating, resulting in a large diesel consumption. At the same time, since there is no preheater for heating in the fourth and fifth stages, the temperature of the flue gas coming from the first absorption tower is relatively low (only about 70°), which easily causes the temperature of the fourth and fifth stages to drop, resulting in huge waste and losses.

[0143] In view of the above problems, this embodiment starts the main fan before the temperature of the conversion catalyst rises, so that the dry suction circulating acid pump circulates normally, and the gas is heated according to the following process:

[0144] First: dry cold air is indirectly heat-exchanged with the high-temperature air of the fuel burner through the preheating furnace, and then the dry cold gas converter is heated to the first catalyst layer after the heat exchange, and the dry cold gas after the heat exchange is passed through the first catalyst layer, the second catalyst layer and the third catalyst layer, so as to gradually heat up the dry cold gas after the heat exchange, and finally the catalyst is heated to heat up, and the used excess waste heat gas enters the first absorption tower, so that part of the gas is input into the large pipe of the second absorption tower through the pipeline of the first absorption tower, thereby reducing the waste heat gas from entering the fourth catalyst layer and the fifth catalyst layer, so that the flue gas can achieve the effect of rapid temperature increase of the conversion catalyst in the first catalyst layer, the second catalyst layer and the third catalyst layer;

[0145] Secondly, a part of the dry cold air is heated by an electric furnace, and then the heated dry cold air enters the four-stage catalyst layer, so that the heated dry cold air is gradually heated in the four-stage catalyst layer and the five-stage catalyst layer. At the same time, the used excess waste heat gas is input into the second absorption tower to merge with the waste heat gas from the first absorption tower, and finally discharged into the atmosphere from the chimney after being processed by the tail absorption tower.

[0146] Through the above technical improvements, the first diesel heating and the second electric furnace heating are connected to the existing sulfuric acid device, and the tail gas heated by the conversion can effectively meet the emission standards, and the heating process of the conversion catalyst is shortened, the energy consumption loss of the conversion catalyst is reduced, and the innovation ability of the existing domestic sulfuric acid devices is greatly improved; such as:

[0147] By using a preheater using diesel combustion as the heating medium for primary conversion and heating, the dry cold air from the main fan is heated by this type of preheater, and an electric furnace is used as the heating medium for secondary conversion and heating, and the low-temperature residual gas after primary conversion and heating from the top of an absorption tower is heated by this circuit; the specific implementation is as follows: start the main fan, and before the main fan exceeds the critical speed, adjust the oil volume and air volume of the fuel burner to increase the temperature of the preheating furnace. Note that the temperature control indicators are: preheater bottom temperature: <700℃, preheater shell outlet temperature: ≤460℃; at the same time, The low-temperature residual gas after conversion and heating is connected to a temporary pipe at the top exhaust valve of the first absorption tower and enters the top outlet pipe of the second absorption tower, and then is sent to the tail absorption tower for washing and purification, and then discharged through the chimney; the DCS system is used to adjust the oil injection amount and air volume of the preheating furnace according to the temperature change of the catalyst layer to ensure that the first stage inlet temperature of the converter is within the index range. When the first stage inlet temperature of the converter rises to 350℃ and the fourth stage inlet temperature is about 220℃, the opening of the first absorption tower top exhaust valve is adjusted, and the fourth stage inlet electric furnace of the converter is turned on to heat the secondary conversion gas with the electric furnace, thereby realizing double conversion and heating of the sulfuric acid unit.

[0148] Regarding the method in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0149] The scheme of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, refer to the relevant description of other embodiments. Those skilled in the art should also know that the actions and modules involved in the description are not necessarily required for the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0150] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for controlling temperature rise in a sulfuric acid plant, characterized in that: The following steps are involved: Step 1: Divide the heating process of the electric furnace into several sub-periods, keep the duration of each sub-period equal, and monitor the heating process of the electric furnace in different sub-periods in real time; Step 2: In each sub-period, according to the performance requirements of the vanadium catalyst, the power output standard of the electric furnace is set, the power output of the electric furnace is monitored in real time through the DCS system, and the power output is analyzed and processed abnormally to obtain the power output floating index; Step 3: Install a temperature sensor close to the surface of the vanadium catalyst, obtain the actual heating rate of the vanadium catalyst in the catalytic reaction zone in each sub-period, and compare it with the expected heating rate to calculate the heating deviation, and obtain the heating rate control deviation index; Step 4: Put the power output floating index and the actual heating speed in the same sub-period, and put them in the same sub-period for comprehensive analysis with the set temperature deviation, establish a temperature control abnormality perception model for comprehensive analysis, generate a temperature control abnormality evaluation coefficient, and perform intelligent evaluation through the temperature control abnormality in the electric furnace heating process; Step 5: If there is temperature control abnormality during the heating process of the electric furnace, the heating process of the electric furnace visualized by the DCS system is used to perform difference analysis between the actual heating curve drawn and the predetermined heating target curve to confirm the abnormality of the heating process of the electric furnace and obtain the difference index; Step 6: Based on the difference index, the heating process of the electric furnace is timely regulated and controlled, and the regulation process is monitored in real time to ensure that the heating process of the electric furnace is successfully regulated.

2. A method for controlling temperature rise in a sulfuric acid plant according to claim 1, characterized in that: The abnormal analysis process includes: The power output of the electric furnace is obtained from the power metering device of the electric furnace, and the power output of the electric furnace is represented by a function P(t) according to a time series, where t represents time; Divide each sub-period into n sub-periods of equal length, where n is a positive integer; Calculate the average of the average power outputs of all sub-periods within the entire sub-period to determine the standard power level for the entire sub-period: represents the average power output of the electric furnace in the entire sub-period. represents the average power output of the electric furnace during the sub-period; Calculate the power output floating index, the power output floating index P out It is the maximum absolute value of the difference between the average power of all sub-periods and the average power of the entire sub-period, reflecting the maximum fluctuation of power output. The calculation expression is:

3. A method for controlling temperature rise in a sulfuric acid plant according to claim 2, characterized in that: The calculating the average value of the average power output of all sub-sub-periods in the entire sub-period comprises: The expression for calculating its average power output is: The duration of each sub-period is Δt. For each sub-period; t k = kΔt, indicating the start time of the kth sub-period, It represents the average power output of the electric furnace in the sub-period.

4. A method for controlling temperature rise in a sulfuric acid plant according to claim 1, characterized in that: The method of obtaining the actual heating rate of the vanadium catalyst in the catalytic reaction zone and comparing it with the expected heating rate to calculate the temperature deviation includes: In each sub-period, the temperature change of the vanadium catalyst is directly captured by a temperature sensor installed in the catalytic reaction zone, and the temperature output of the sensor is represented by the function Tem(t) according to the time series; Using continuous temperature readings, the actual heating rate is calculated using a differential formula; At any time t, the heating rate deviation is calculated from the difference between the actual heating rate and the expected heating rate. The calculation expression is: d(t) = r actual (t)-r expected , where d(t) represents the deviation between the actual heating rate and the expected heating rate at time t, r actual (t) represents the actual heating rate at time t, r expected represents the expected heating rate at time t; According to the expression: The heating speed control deviation index is calculated, where HR di represents the heating speed control deviation index, M represents the total duration of the sub-period, and t1 and t2 are the starting point and end point of the sub-period, respectively.

5. A method for controlling temperature rise in a sulfuric acid plant according to claim 4, characterized in that: The difference formula is: Among them, r actual (t) represents the actual heating rate at time t, and Δt is the time interval between two adjacent temperature readings.

6. A method for controlling temperature rise in a sulfuric acid plant according to claim 1, characterized in that: After the temperature control abnormality assessment coefficient is generated, the following steps are included: The temperature control abnormality assessment coefficient is compared and analyzed with a preset temperature control abnormality assessment coefficient reference threshold value, If the temperature control abnormality assessment coefficient is greater than or equal to the temperature control abnormality assessment coefficient reference threshold, a temperature control hidden danger signal is issued; If the temperature control abnormality evaluation coefficient is less than the temperature control abnormality evaluation coefficient reference threshold, a temperature control high efficiency signal is issued.

7. A method for controlling temperature rise in a sulfuric acid plant according to claim 6, characterized in that: After the temperature control hidden danger signal is sent, the method further includes: The temperature control abnormality assessment coefficients generated in the subsequent several sub-periods are obtained to establish an analysis set for comprehensive analysis. The comprehensive analysis process includes the following steps: By analyzing the temperature control anomaly assessment coefficients in the set, the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are calculated, and the mean value of the temperature control anomaly assessment coefficient and the standard deviation of the temperature control anomaly assessment coefficient are compared and analyzed with the pre-set temperature control anomaly assessment coefficient reference threshold and standard deviation reference threshold respectively; If the mean value of the temperature control abnormality assessment coefficient is less than the reference threshold value of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold value of the standard deviation, a sudden signal is issued, indicating that there is a sudden temperature control abnormality risk in the heating process of the electric furnace; If the mean of the temperature control abnormality assessment coefficient is less than the reference threshold of the temperature control abnormality assessment coefficient and the standard deviation of the temperature control abnormality assessment coefficient is less than the reference threshold of the standard deviation, a risk signal is issued, indicating that there is no hidden danger of sudden temperature control abnormality in the heating process of the electric furnace.

8. A method for controlling temperature rise in a sulfuric acid plant according to claim 1, characterized in that: The difference analysis includes: Derive the actual temperature curve data T of the electric furnace from the DCS system ac (t) and the predetermined target temperature curve data T arge (t), where t represents the measurement time point; Align the time points to ensure that the two sets of data T ac (t) and T arge (t) comparisons are made at the same time intervals and lengths; The actual temperature data T ac (t) and target temperature data T arge (t) is regarded as two vectors, expressed as and According to the cosine similarity formula, we can calculate and The cosine similarity Co sim ; Based on the expression: θ diff =1-Cos sim The difference index is calculated as follows: Represents the difference index.

9. A method for controlling temperature rise in a sulfuric acid plant according to claim 1, characterized in that: The timely regulation of the heating process of the electric furnace based on the difference index includes: Comparing and analyzing the difference index with a preset difference index reference threshold; If the difference index is less than the difference index reference threshold, a control success signal is generated, indicating that the control is successful; If the difference index is greater than or equal to the difference index reference threshold, a control failure signal is generated, indicating that the control has failed and that control needs to be continued.

10. A method for controlling temperature rise in a sulfuric acid plant according to claim 8, characterized in that: The cosine similarity formula is: Among them, the dot product Cos sim express and The cosine similarity of .

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