Sulfuric acid material balancing system and method

Abstract

The invention relates to the technical field of intelligent production data monitoring, in particular to a sulfuric acid material balancing system and method. The system comprises a data acquisition module used for acquiring data in a sulfuric acid production process and verifying the data; the balance calculation module is used for triggering material balance calculation in time according to the operation instruction and the updating condition of the production data; the model building module is used for building a sulfuric acid production material balance data model by adopting a mathematical modeling method based on the results of the process flow analysis and the parameter determination; the analysis mining module is used for carrying out deep analysis on the production data by applying a data mining algorithm, and presenting a data analysis and mining result in a visual mode; and the data management module is used for storing various data in the sulfuric acid production process, quickly querying required data according to conditions, and carrying out maintenance and safety management on a database. The technical scheme can improve the sulfuric acid production efficiency and ensure the product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production data monitoring, and in particular to a sulfuric acid material balance system and method. Background Art

[0002] Sulfuric acid, as an important basic chemical raw material, is widely used in a variety of fields, including fertilizers, metallurgy, petrochemicals, textiles, national defense, pesticides, and pharmaceuticals. With the continuous advancement of industrial technology, the scale of sulfuric acid production has continued to expand, and the production process has become increasingly complex. Currently, sulfuric acid production primarily utilizes the contact process, which includes key steps such as roasting, conversion, and absorption. During the production process, even slight changes in operating parameters such as raw material feed rate, reaction temperature, and pressure can significantly impact product quality, production efficiency, and energy consumption.

[0003] The existing sulfuric acid production management model lacks real-time, accurate monitoring and scientific analysis of the production process. Summary of the Invention

[0004] The purpose of the present invention is to provide a sulfuric acid material balance system and method, which can improve sulfuric acid production efficiency and ensure product quality.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a sulfuric acid material balance system, comprising:

[0006] Data acquisition module, used to collect data from the sulfuric acid production process and verify the data;

[0007] Balance calculation module, used to trigger material balance calculation in time according to operation instructions and production data updates;

[0008] A model building module is used to construct a sulfuric acid production material balance data model using mathematical modeling methods based on the results of process flow analysis and parameter determination;

[0009] The analysis and mining module is used to apply data mining algorithms to conduct in-depth analysis of production data and present the data analysis and mining results in a visual manner;

[0010] The data management module is used to store various data in the sulfuric acid production process, quickly query required data according to conditions, and maintain and securely manage the database.

[0011] The basic solution's benefits include timely triggering of material balance calculations based on operational instructions and production data updates, ensuring the timeliness of these calculations. In sulfuric acid production, production conditions such as raw material feed rate, reaction temperature, and pressure can change at any time. Timely material balance calculations can quickly reflect the impact of these changes on the entire production process.

[0012] The material balance data model accurately describes the material flow, transformation, and equilibrium relationships during sulfuric acid production. For example, by analyzing each major process flow, such as roasting, conversion, and absorption, and identifying key parameters for each link, the constructed model can simulate material changes under different operating conditions. Based on the model's predictions, production personnel can proactively adjust production parameters and optimize the production process, making it more scientific and rational.

[0013] Using data mining algorithms to conduct in-depth analysis of production data can uncover valuable information hidden in large amounts of data, and present the data analysis and mining results in a visual way, allowing production managers to understand the data more intuitively.

[0014] The system stores various data from the sulfuric acid production process and enables quick, conditional data retrieval, greatly facilitating production management. Database maintenance and security management ensure data integrity and security. Sulfuric acid production data contains core enterprise production information, including sensitive information such as production processes and raw material formulas. Effective database maintenance and security management measures, such as data backup and access control, prevent data loss, leakage, and malicious tampering, ensuring stable system operation.

[0015] As an implementable and optimal solution, various sensors and instruments at the sulfuric acid production site collect data from the production process in real time. The collected data includes material flow, concentration, temperature, and pressure. It also supports manual input or importing historical data and experimental data from other systems.

[0016] As an implementable preferred solution, the balance calculation module is used to store the calculated material balance results in a database, and store the results according to time and production batch.

[0017] As an implementable preferred solution, the model construction module uses a mathematical modeling method to construct a sulfuric acid production material balance data model based on the results of process flow analysis and parameter determination. The model describes the material balance relationship of each production link in the form of an equation group, and uses historical production data to verify the constructed model. The model calculation results are compared with actual production data to evaluate the accuracy of the model. The model is optimized according to the verification results, and the model parameters or structure are adjusted.

[0018] As an implementable preferred solution, the analysis and mining module is used to display the data analysis and mining results in the form of a flowchart. The flowchart includes several points, and different points represent different meanings. Clicking each point can view the corresponding theoretical reference value or actual production value. When a problem occurs with the equipment, the point mark is displayed in orange and red.

[0019] As an implementable preferred solution, the data management module is used to store various types of data in the sulfuric acid production process, including material data, production process data, equipment operation data, and material balance calculation results. A relational database or a non-relational database is used, and a suitable storage method is selected according to the data characteristics and application requirements. A relational database or a non-relational database is used, and a storage method is selected according to the data characteristics and application requirements.

[0020] As an implementable preferred solution, the system further includes an abnormality reminder module, which is used to send a reminder signal in time when abnormal data or equipment failure is detected.

[0021] As an implementable preferred solution, the model building module calls the historical data collected by the acquisition submodule, or the historical data input or imported, and builds a verification threshold adjustment model through a machine learning algorithm. The model is trained using the sorted historical data, and the data is divided into a training set and a test set according to a certain ratio. During the training process, the production season and raw material batch are used as input features, and a reasonable data verification range is used as an output label. By continuously adjusting the parameters of the model, the model learns the relationship between different production conditions and the data verification range.

[0022] As an implementable preferred solution, the check submodule in the balance calculation module uses a dynamically adjusted check threshold to check the collected data. The check rules include data range check, data change trend check, and data correlation check.

[0023] In a second aspect, the present invention provides a sulfuric acid material balance method, which utilizes the above-mentioned sulfuric acid material balance system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a structural diagram of a sulfuric acid material balance system.

[0025] Figure 2 This is a schematic diagram showing the points of a sulfuric acid material balance system.

[0026] Figure 3 This is an operational flow chart of a sulfuric acid material balance system.

[0027] Figure 4 This is a data mining diagram for a sulfuric acid material balance system.

[0028] Figure 5 Schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It will be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain the present application, rather than to limit the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered to be isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0030] In addition, unless otherwise defined, technical or scientific terms used in the description of the present invention should have the common meanings understood by those skilled in the art in the art to which the present invention belongs.

[0031] The present invention will be further described in detail below with reference to the accompanying drawings:

[0032] Reference numerals: electronic device 500 , processor 501 , communication interface 502 , memory 503 , bus 504 .

[0033] Example 1

[0034] Reference Figure 1 The embodiment of the present disclosure provides a sulfuric acid material balance system, including a data acquisition module, a balance calculation module, a model building module, an abnormality reminder module, an analysis and mining module, and a data management module.

[0035] The data acquisition module includes an acquisition submodule and a verification submodule.

[0036] The acquisition submodule uses various sensors and instruments at the sulfuric acid production site to collect real-time data on material flow, concentration, temperature, pressure, and other aspects of the production process. It also supports manual input or the import of historical and experimental data from other systems to ensure data integrity. The validation submodule rigorously verifies the collected data, checking its rationality, accuracy, and completeness. Verification rules include range verification, data trend verification, and data correlation verification, and provides prompts when abnormal data is detected.

[0037] The balance calculation module includes the algorithm core submodule, the task scheduling submodule, and the storage management submodule.

[0038] The core submodule of the algorithm is used to apply the material balance algorithm, combining the chemical reaction stoichiometry, material loss rate, conversion rate and other key factors of sulfuric acid production for accurate calculation. The algorithm is based on the law of conservation of mass, taking into account multi-step chemical reactions and complex production processes.

[0039] The calculation formula is as follows:

[0040] Sulfur = sulfur consumption (liquid sulfur consumption) * liquid sulfur purity (liquid sulfur consumption, the value after 99.8 is similar except 100) / 100

[0041]

[0042] Heat brought by liquid sulfur = liquid sulfur consumption (liquid sulfur consumption) * 178.32

[0043] Heat after liquid sulfur combustion = liquid sulfur consumption (liquid sulfur consumption) * 9861

[0044] Total heat entering the sulfur incinerator = heat brought into the furnace by the air + heat brought into the liquid sulfur + heat after the liquid sulfur is burned

[0045] Total heat taken out of the sulfur incinerator = total heat entering the sulfur incinerator * heat loss rate

[0046]

[0047] Total heat carried out by the sulfur incinerator outlet gas per °C = sulfur dioxide + sulfur trioxide oxygen + nitrogen

[0048]

[0049] Total flue gas flow = air volume (standard state) (air consumption) - SO3 generation (furnace gas concentration, volume unit) * 0.5

[0050]

[0051] Main line high temperature flue gas flow = total flue gas flow - auxiliary line high temperature flue gas flow

[0052] Amount of sulfur reacted = sulfur consumption (liquid sulfur consumption) / 32.06

[0053] SO3 production = amount of sulfur reacting material * 0.029

[0054] Remaining SO2 = amount of sulfur reacted - amount of SO3 produced

[0055] Oxygen consumption = amount of sulfur reacted + amount of SO3 produced * 0.5

[0056]

[0057] Heat brought in by sulfur = sulfur consumption (liquid sulfur consumption) * 178.32

[0058] Heat after liquid sulfur combustion = sulfur consumption (liquid sulfur consumption) * 9861

[0059]

[0060] Average heat capacity of sulfur dioxide = 26.04 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature 2 +0.861*10 -8 *Average temperature 3

[0061] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0062] Average heat capacity of oxygen = 26.04 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0063] Average heat capacity of nitrogen = 27.48 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature 2

[0064] Heat required per degree Celsius

[0065] SO2=residual SO2*average heat capacity of sulfur dioxide

[0066] SO3=sulfur trioxide production*average heat capacity of sulfur trioxide

[0067]

[0068] O2'=Oxygen consumption*average heat capacity of oxygen

[0069]

[0070] A=SO2+SO3+O

[0071] Total heat taken out 1 = A*(outlet temperature - inlet temperature)

[0072] Total heat taken out 2 = (O2 + N2) * (outlet temperature - inlet temperature)

[0073] B = (heat brought in by liquid sulfur + heat from liquid sulfur combustion) * (1-heat loss rate) - total heat brought out 1

[0074] C = total heat taken out 2 - heat taken in by air entering the furnace * (1 - heat loss rate)

[0075]

[0076] Air consumption = D*0.8662

[0077] Furnace gas concentration

[0078]

[0079]

[0080] Latent heat of vaporization = -0.0265*boiler steam temperature 2 +8.0217*Boiler Steam Temperature+1363.1

[0081] Heat absorbed per kg of water = latent heat of vaporization + 4.85 * (boiler steam temperature - boiler feed water temperature)

[0082]

[0083] Average heat capacity of sulfur dioxide = 26.04 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature 2 +0.861*10 -8 *Average temperature 3

[0084] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0085] Average heat capacity of oxygen = 26.04 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0086] Average heat capacity of nitrogen = 27.48 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature 2

[0087] A section of import

[0088] The amount of SO2 = SO2 concentration in furnace gas (air consumption) * flue gas flow (bell valve flow control) / 22.4

[0089] The amount of SO3 = SO3 concentration in furnace gas (air consumption) * flue gas flow (bell valve flow control) / 22.4

[0090] The amount of O2 substance = furnace gas concentration O2 (air consumption) * flue gas flow (bell valve flow control) / 22.4

[0091] Amount of N2 substance = furnace gas concentration N2 (air consumption) * flue gas flow (bell valve flow control) / 22.4 flue gas

[0092] Heat absorbed per 1°C drop in temperature = average heat capacity of sulfur dioxide * amount of SO2 + average heat capacity of sulfur trioxide * amount of SO3 + average heat capacity of oxygen * amount of O2 + average heat capacity of nitrogen * amount of N2 Total heat absorbed = heat absorbed per 1°C drop in flue gas * (heat loss boiler inlet gas temperature - heat loss boiler outlet gas temperature)

[0093]

[0094] A transformation

[0095]

[0096] Average heat capacity of sulfur dioxide = 26.04 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature + 0.861*10 -8 *Average temperature 3

[0097] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0098] Average heat capacity of oxygen = 26.04 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0099] Average heat capacity of nitrogen = 27.48 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature

[0100] The amount of sulfur dioxide = flue gas flow rate * furnace gas concentration SO2 (furnace gas temperature) / 22.4

[0101] The amount of sulfur trioxide = flue gas flow rate * furnace gas concentration SO3 (furnace gas temperature) / 22.4

[0102] Amount of oxygen substance = flue gas flow rate * furnace gas concentration O2 (furnace gas temperature) / 22.4

[0103] Amount of nitrogen = flue gas flow rate * furnace gas concentration N2 (furnace gas temperature) / 22.4

[0104] Heat required for each 1°C temperature rise = average heat capacity of sulfur dioxide * amount of sulfur dioxide + average heat capacity of sulfur trioxide * amount of sulfur trioxide + average heat capacity of oxygen * amount of oxygen + average heat capacity of nitrogen * amount of nitrogen

[0105] First stage conversion rate = (outlet temperature - inlet temperature) * heat required for each 1°C temperature rise * 100 * (1-heat loss) / 99227.2 / amount of SO2 in the first stage Second stage conversion

[0106]

[0107] Average heat capacity of sulfur dioxide = 26.04 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature 2 +0.861*10 -8 *Average temperature 3

[0108] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0109] Average heat capacity of oxygen = 26.04 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0110] Average heat capacity of nitrogen = 27.48 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature 2

[0111] The amount of sulfur dioxide = the amount of SO2 converted in one stage - the amount of SO2 converted in one stage * the conversion rate in one stage / 100

[0112] The amount of sulfur trioxide = the amount of SO3 converted in the first stage + the amount of SO2 converted in the first stage * the conversion rate in the first stage / 100

[0113] The amount of oxygen substance = the amount of O2 substance converted in the first stage - the amount of SO2 substance converted in the first stage * the conversion rate of the first stage / 200

[0114] The amount of nitrogen substance = the amount of nitrogen substance in one section

[0115] Heat required for each 1°C temperature rise = average heat capacity of sulfur dioxide * amount of sulfur dioxide + average heat capacity of sulfur trioxide * amount of sulfur trioxide + average heat capacity of oxygen * amount of oxygen + average heat capacity of nitrogen * amount of nitrogen

[0116] Second stage conversion rate'=(outlet temperature - inlet temperature)*heat required for each 1℃ temperature rise*100*(1-heat loss) / 99227.2 / first stage inlet SO2 material

[0117] Second stage conversion rate = Second stage conversion rate + First stage conversion rate

[0118] Three-stage transformation

[0119]

[0120] Average heat capacity of sulfur dioxide = 25.64 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature 2 +0.861*10 -8 *Average temperature 3

[0121] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0122] Average heat capacity of oxygen = 25.64 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0123] Average heat capacity of nitrogen = 27.18 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature 2

[0124] The amount of sulfur dioxide = the amount of SO2 converted in the second stage - the amount of SO2 converted in the second stage * the second stage conversion rate / 100

[0125] The amount of sulfur trioxide = the amount of SO3 converted in the second stage + the amount of SO2 converted in the second stage * the second stage conversion rate / 100

[0126] Amount of oxygen material = Amount of O2 material converted in the second stage - Amount of SO2 material converted in the second stage * Second stage conversion rate' / 200

[0127] The amount of nitrogen substance = the amount of nitrogen substance in one section

[0128] The amount of heat required for each 1°C temperature rise = average heat capacity of sulfur dioxide * amount of sulfur dioxide + average heat capacity of sulfur trioxide * amount of sulfur trioxide +

[0129] Average heat capacity of oxygen * amount of oxygen substance + average heat capacity of nitrogen * amount of nitrogen substance

[0130] The third stage conversion rate'=(outlet temperature inlet temperature)*heat required for each 1℃ temperature rise*100*(1-heat loss) / 99227.2 / first stage imported SO2 material third stage conversion rate third stage conversion rate'+second stage conversion rate

[0131] Four-stage transformation

[0132]

[0133] Average heat capacity of sulfur dioxide = 25.64 + 5.8 * 10 -2 *Average temperature -38.1*10 -6 *Average temperature 2 +0.861*10 -8 *Average temperature 3

[0134] Average heat capacity of sulfur trioxide = 15.09 + 15.2 * 10 -2 *Average temperature -120.7*10 -6 *Average temperature 2 +3.62*10 -8 *Average temperature 3

[0135] Average heat capacity of oxygen = 25.64 + 1.3 * 10 -2 *Average temperature - 3.86*10 -6 *Average temperature 2

[0136] Average heat capacity of nitrogen = 27.18 + 0.591 * 10 -2 *Average temperature - 0.338*10 -6 *Average temperature 2

[0137] The amount of sulfur dioxide at the third stage outlet = the amount of SO2 converted in the third stage - the amount of SO2 converted in the third stage * the conversion rate of the second stage / 100

[0138] The amount of sulfur trioxide at the third stage outlet = the amount of SO3 converted in the third stage + the amount of SO2 converted in the third stage * the conversion rate of the third stage / 100

[0139] The amount of oxygen at the outlet of the third stage = the amount of O2 converted in the third stage - the amount of SO2 converted in the third stage * the conversion rate of the third stage / 200

[0140] The amount of nitrogen at the outlet of the third stage = the amount of nitrogen at the first stage

[0141] The amount of sulfur dioxide at the inlet of the fourth stage = the amount of sulfur dioxide at the outlet of the third stage

[0142] The amount of sulfur trioxide at the fourth stage inlet = the amount of sulfur trioxide at the third stage outlet * 0.001

[0143] The amount of oxygen substance at the fourth stage inlet = the amount of oxygen substance at the third stage outlet

[0144] The amount of heat required for each 1°C temperature rise = average heat capacity of sulfur dioxide * amount of sulfur dioxide + average heat capacity of sulfur trioxide * amount of sulfur trioxide +

[0145] Average heat capacity of oxygen * amount of oxygen substance + average heat capacity of nitrogen * amount of nitrogen substance

[0146] The conversion rate of the fourth stage = (outlet temperature - inlet temperature) * heat required for each 1 ° C temperature increase * 100 * (1-heat loss) / 99227.2 / the fourth stage conversion rate of the first stage imported SO2 = the fourth stage conversion rate + the third stage conversion rate

[0147] The task scheduling submodule is used to schedule calculation tasks according to the update of operation instructions and production data. When new material data is input or process parameters are adjusted, the material

[0148] Balance calculations to ensure real-time calculation results.

[0149] The storage management submodule is used to store the calculated material balance results in a database for easy query and traceability. The results are categorized and stored by time, production batch, and other dimensions to facilitate data analysis and mining.

[0150] The model building module uses mathematical modeling to construct a material balance data model for sulfuric acid production based on process flow analysis and parameter determination. This model describes the material balance relationships across each production step as a system of equations, accurately reflecting the material transformations and energy changes during the production process. The constructed model is validated using historical production data, and the model's calculated results are compared with actual production data to assess its accuracy. Based on these validation results, the model is optimized, adjusting model parameters or structure to improve its accuracy and adaptability.

[0151] Taking the example of calculating the conversion rate from a known converter temperature, for a four-stage reactor, using temperature to calculate the conversion rate primarily considers the principle of heat conservation. For an insulated converter, the heat released by the reaction is primarily used to heat the gas. Therefore, after accounting for some heat losses, the temperature rise of the gas is the temperature released by the reaction in that stage. Heat capacity is the primary metric for determining the heat released. The average heat capacity of each of the four gases is calculated using their average temperatures. Using the previously calculated gas volume fractions and flow rates, the amount of substance used is calculated (assuming ideal gases, the volume flow rate is divided by 22.4). The average heat capacity of each gas is multiplied by its amount of substance. The sum of the four gives the heat required for a 1°C increase. Multiplying this by the difference in inlet and outlet temperatures yields the heat released by the reaction. Taking into account reasonable heat losses in each stage yields the corresponding heat of reaction. After some research, I learned that the reaction of sulfur dioxide to sulfur trioxide releases 99,227.2 kJ / kmol of heat. Dividing the heat of reaction by 99,227.2 gives the amount of sulfur dioxide produced. Dividing this by the amount of sulfur dioxide introduced into stage 1 gives the amount of sulfur dioxide produced in that stage. The subsequent conversion rate is the sum of the previous stage's conversion rate and the final conversion rate for that stage.

[0152] For reactions after stage 1, the impact of the previous reactions needs to be considered. The amount of sulfur dioxide in the next stage is basically the amount of sulfur dioxide in the previous stage minus the amount of sulfur dioxide in the previous stage multiplied by the conversion rate of the previous stage. The amount of sulfur trioxide in the next stage is the amount of sulfur trioxide in the previous stage plus the amount of sulfur dioxide in the previous stage multiplied by the conversion rate of the previous stage. For oxygen, it is the amount of oxygen in the previous stage minus the amount of sulfur dioxide in the previous stage multiplied by the conversion rate of the previous stage multiplied by 0.5. Nitrogen does not participate in the reaction at all, so the amount of nitrogen remains unchanged throughout the process. In the fourth stage of conversion, sulfur trioxide is basically absorbed due to the process, so the amount of material entering the fourth stage is multiplied by 0.001 to indicate absorption.

[0153] The analysis and mining module includes a preprocessing submodule, a data mining submodule, and a data display submodule.

[0154] The preprocessing submodule is used to perform preprocessing operations such as cleaning, denoising, and normalization on the production data stored in the database to improve data quality.

[0155] The data mining submodule is used to conduct in-depth analysis of production data using data mining algorithms such as association rule mining, cluster analysis, and predictive analysis. It can also uncover potential patterns and rules in the production process, such as the relationship between material consumption and product quality, and factors affecting production efficiency.

[0156] The data display submodule is used to present the data analysis and mining results in a visual way (such as a flow chart, etc.), refer to Figure 2 , including several points, different points represent different meanings, according to Figure 3 The process can view the meaning of different points. For example, point 1 represents the liquid sulfur inlet of the sulfur incinerator, and the data displayed can include sulfur consumption data (t / h) and liquid sulfur inlet temperature data (℃).

[0157] Point 2 represents the fan inlet air, and the data that can be displayed are the pressure before the fan (gauge pressure kPa), the pressure after the fan (gauge pressure (kPa), and the temperature before the fan (℃).

[0158] Point 3 represents the air at the fan outlet, and the data that can be displayed are air water content (kg / h), air volume (standard state) (m 3 / h), temperature behind the fan in front of the tower (℃), temperature behind the fan in the back of the tower (℃).

[0159] Point 4 represents dry air, and the data that can be displayed are dry air inlet temperature (℃), dry air volume entering the furnace (standard state) (m 3 / h).

[0160] Point 5 represents the outlet gas of the sulfur incinerator, and the data that can be displayed are gas concentration (%), outlet temperature (℃), SO2, gas concentration, SO3, O2, and outlet temperature (℃).

[0161] Point 6 represents the exhaust gas at the waste heat boiler outlet. The data that can be displayed are the exhaust gas temperature at the waste heat boiler outlet (°C), boiler feed water temperature (°C), boiler pressure (mpa), and continuous pressure (kpa).

[0162] Point 7 represents the flow regulation of the bell valve, and the data that can be displayed are the secondary pipeline high-temperature flue gas flow (%) and the main pipeline high-temperature flue gas flow (%).

[0163] Point 8 represents a section of inlet, and the data that can be displayed is the inlet temperature (℃) of a section.

[0164] Point 9 represents the first stage outlet, and the data that can be displayed are the first stage outlet temperature (°C), the first stage conversion rate (%), the higher steam inlet temperature (°C), and the higher steam outlet temperature (°C).

[0165] Point 10 represents the second stage inlet, and the data that can be displayed is the second stage inlet temperature (℃).

[0166] Point 11 represents the second stage outlet, and the data that can be displayed are the second stage outlet temperature (°C) and the second stage conversion rate (%).

[0167] Point 12 represents the three-stage inlet, and the data that can be displayed is the three-stage inlet temperature (℃).

[0168] Point 13 represents the three-stage outlet, and the data that can be displayed are the three-stage outlet temperature (°C) and the three-stage conversion rate (%).

[0169] Point 14 represents the outlet of the cold exchange pipe, and the data that can be displayed is the hot gas outlet temperature (℃).

[0170] Point 15 represents the flue gas at the HRS tower inlet, and the data that can be displayed is the hot flue gas inlet temperature (℃).

[0171] Point 16 represents the flue gas at the HRS tower outlet, and the data that can be displayed is the flue gas outlet temperature (℃).

[0172] Point 17 represents the outlet of the cold shell exchange process, and the data that can be displayed is the inlet temperature (℃) of the hot shell exchange process.

[0173] Point 18 represents the four-stage inlet, and the data that can be displayed is the four-stage inlet temperature (℃).

[0174] Point 19 represents the four-stage outlet, and the data that can be displayed are the four-stage outlet temperature (°C) and the four-stage conversion rate (%).

[0175] Point 20 represents a temperature lower than the outlet, and the displayed data is the outlet flue gas temperature (℃).

[0176] Point 21 represents the outlet of province 1, and the data that can be displayed is the outlet temperature of the flue gas of province 1 (℃).

[0177] Point 22 represents the flue gas at the second suction outlet, and the data displayed are the second suction tower flue gas outlet temperature (°C), oxygen content (%), sulfur dioxide content (mg / m 3 ).

[0178] Point 23 represents the acid entering the drying tower, and the data displayed are sulfuric acid flow (m 3 / h), drying tower acid temperature (℃), drying tank acid concentration (%).

[0179] Point 24 represents the acid output from the drying tower, and the data that can be displayed are the acid concentration (%), acid output from the drying tower (kg / h), and acid temperature (°C).

[0180] Point 25 represents the acid entering the second absorption tower, and the data displayed are sulfuric acid flow (m 3 / h), second absorption tower acid temperature (℃), acid concentration (second absorption tower acid tank acid concentration) %, HRS acid feed acid temperature (℃).

[0181] Point 26 represents the acid output from the second absorption tower, and the data that can be displayed are the acid concentration (%), acid volume (kg / h), and acid temperature (°C) of the second absorption tower.

[0182] Point 27 represents the product acid, and the data that can be displayed are product acid concentration (%), product acid amount (kg / h), and product acid temperature (°C).

[0183] Point 28 represents the acid absorption capacity of the drying string, and the data that can be displayed is the acid absorption capacity of the drying string (kg / h).

[0184] Point 29 represents the HRS acid content of the drying train, and the data that can be displayed is the HRS acid content of the drying train (kg / h).

[0185] Point 30 represents HRS secondary acid, and the data displayed is the amount of HRS upper tower secondary acid (upper tower acid) (m 3 / h), temperature (℃)

[0186] Point 31 represents the acid in the HRS lower tower, and the data that can be displayed are the acid concentration in the HRS lower tower (%), the acid temperature in the HRS lower tower ℃, the acid amount in the HRS lower tower kg / h, and the steam pressure.

[0187] Point 32 represents the diluter water addition, which can display the data of diluter water addition volume (kg / h), diluter inlet acid temperature (℃), factory air flow (m 3 / h)

[0188] Point 33 represents HRS first-grade acid, which can display the data of first-grade acid inlet temperature (℃), first-grade acid inlet volume (m 3 / h), primary acid concentration (%).

[0189] Point 34 represents the HRS heater, and the data that can be displayed are the heater inlet acid temperature (℃), inlet water temperature (℃), and outlet water temperature (℃).

[0190] Point 35 represents the HRS preheater, and the data that can be displayed are preheater outlet acid temperature (°C), inlet water temperature (°C), outlet water temperature (°C), and HRS preheater water volume (kg / h).

[0191] Point 36 represents the acid amount of the second absorption tank of the HRS acid production series, and the data that can be displayed is the acid amount of the second absorption tank of the HRS acid production series (kg / h).

[0192] Point 37 represents the acid content of the drying tank of the HRS acid production train, and the data that can be displayed is the acid content of the drying tank of the HRS acid production train (kg / h).

[0193] Point 38 represents the second suction water addition, and the data that can be displayed is the second suction water addition amount (kg / h).

[0194] Point 39 represents the circulating water temperature, and the data that can be displayed are circulating water inlet temperature (°C), circulating water outlet temperature (°C), deaerator temperature (°C), and steam pressure (kpa).

[0195] Point 40 represents the dry acid meter, which can display the data of the dry acid cooler circulating water consumption (kg / h).

[0196] Point 41 represents the secondary absorption acid cooler, and the data that can be displayed is the circulating water consumption (kg / h) of the secondary absorption acid cooler.

[0197] Point 42 represents the HRS secondary acid cooler, and the data that can be displayed are the outlet acid temperature (℃) and the circulating water consumption of the HRS acid cooler (kg / h).

[0198] Point 43 represents the finished acid cooler, and the data that can be displayed are the outlet acid temperature (℃) and the finished acid cooler circulating water consumption (kg / h).

[0199] When the equipment is operating normally, each point is green. When a problem occurs, the point mark is orange and red. At the same time, you can enter the actual production data at the bottom of the page, and the system will automatically record it in the database.

[0200] The abnormal reminder module is used to send out reminder signals in time when abnormal data or equipment failure is detected. It specifically includes real-time monitoring of material balance calculation results and production data. Figure 4 By setting thresholds and early warning rules, abnormal material balance conditions can be detected in a timely manner. Monitoring indicators include material flow deviation, concentration abnormalities, conversion rate fluctuations, etc.

[0201] The data management module includes a storage submodule, a query submodule, and a maintenance submodule.

[0202] The storage submodule is used to store various data from the sulfuric acid production process, including material data, production process data, equipment operation data, material balance calculation results, etc. It uses either a relational or non-relational database, selecting the appropriate storage method based on data characteristics and application requirements.

[0203] The query submodule is used to quickly query the required data based on time, production batch, material name, equipment number and other conditions.

[0204] The maintenance submodule is used to maintain the database, including data backup, data cleanup, data update and other operations to ensure the stability and reliability of the database. It also manages the database security and sets user permissions to prevent data leakage and illegal access.

[0205] Example 2

[0206] The technical feature that distinguishes this embodiment from the above embodiments is that the model building submodule calls the acquisition submodule to collect historical data such as material flow, concentration, temperature, pressure, etc., or / and input or imported historical data, and builds a verification threshold adjustment model through a machine learning algorithm.

[0207] The model is trained using collated historical data. The data is divided into training and test sets according to a specific ratio, for example, 70% for training and 30% for testing. During training, production season and raw material batch (especially sulfur purity) are used as input features, and a reasonable data validation range is used as the output label. By continuously adjusting the model parameters, the model learns the relationship between different production conditions and the data validation range.

[0208] According to the trained machine learning model, combined with the real-time collection of production season and raw material batch information (such as sulfur purity), the data verification threshold is dynamically adjusted. In different seasons, due to changes in ambient temperature, humidity and other factors, the reaction rate and material properties in the sulfuric acid production process may change, thereby affecting the reasonable range of the data. When the model detects that it is currently summer, due to the high temperature, the volatilization rate of the material may accelerate, and the verification range of data such as the drying tower inlet air volume and temperature may need to be adjusted accordingly. If the normal range of the drying tower inlet air volume in summer increases by 10%-20% based on the winter, the model will automatically adjust the verification threshold of the data according to this rule.

[0209] Variations in raw material batches, particularly fluctuations in sulfur purity, significantly impact sulfuric acid production. Changes in sulfur purity alter the incinerator's heat of reaction and sulfur dioxide production, affecting subsequent production data. If the sulfur purity in a batch of raw materials is 5% below the standard, the model automatically narrows the calibration range for the incinerator's sulfur injection rate based on learned patterns to ensure production stability.

[0210] The check submodule in the balance calculation module uses the dynamically adjusted check threshold to check the collected data. The check rules include data range check and data change trend check.

[0211] Data range verification: For data under different seasons and raw material batch conditions, determine whether it is within a reasonable range based on the adjusted threshold.

[0212] Data trend verification combines dynamic threshold analysis to determine whether the data trend matches expectations under current production conditions. For example, if sulfur purity is stable during a certain production phase, but the flue gas temperature at the incinerator outlet does not match the model's prediction based on current conditions and exceeds the dynamically adjusted threshold, the system will determine that the data is abnormal.

[0213] Data correlation verification is based on dynamic thresholds. For example, under normal production conditions, there is a certain correlation between the sulfur injection rate of a sulfur incinerator and the flue gas concentration at the incinerator's outlet. This correlation changes as the sulfur purity changes, and the model's adjusted thresholds reflect this change. If the correlation of the actual data exceeds the adjusted threshold range, the system will indicate a data anomaly.

[0214] This embodiment combines a machine learning algorithm to address the impact of sulfuric acid production seasons and raw material batch differences (such as sulfur purity fluctuations) on the data verification range, achieve automatic optimization of the data verification range, and improve the accuracy and effectiveness of data verification.

[0215] The disclosed embodiments also provide a sulfuric acid material balance method, which utilizes the above-mentioned sulfuric acid material balance system.

[0216] Those skilled in the art will appreciate that all or part of the process steps in a sulfuric acid material balance method can be accomplished by instructing related hardware via a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the process steps of various embodiments of a sulfuric acid material balance method. Wherein, any reference to memory, storage, database, or other media used in the various embodiments provided herein can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0217] The present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the sulfuric acid material balance method described above are implemented. In the present application, the processor serves as the control center of the computer method and can be a processor of a physical machine or a processor of a virtual machine.

[0218] Reference Figure 5 The electronic device 500 includes at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. Bus 504 is used to enable communication between these components, communication interface 502 is used to communicate signaling or data with other node devices, and memory 503 stores machine-readable instructions executable by processor 501. When electronic device 500 is in operation, processor 501 communicates with memory 503 via bus 504, and when the machine-readable instructions are invoked by processor 501, the steps of the aforementioned sulfuric acid material balance method are executed.

[0219] The above contents are merely embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. A person of ordinary skill in the art is aware of all common technical knowledge in the technical field to which the invention belongs before the filing date or priority date, is able to obtain all existing technologies in the field, and has the ability to apply conventional experimental means before that date. A person of ordinary skill in the art can, under the guidance of this application, improve and implement this scheme in combination with his or her own abilities. Some typical known structures or known methods should not become an obstacle for a person of ordinary skill in the art to implement this application. It should be pointed out that for a person of ordinary skill in the art, several variations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection claimed in this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A sulfuric acid material balance system, characterized in that: include: Data acquisition module, used to collect data from the sulfuric acid production process and verify the data; Balance calculation module, used to trigger material balance calculation in time according to operation instructions and production data updates; A model building module is used to construct a sulfuric acid production material balance data model using mathematical modeling methods based on the results of process flow analysis and parameter determination; The analysis and mining module is used to apply data mining algorithms to conduct in-depth analysis of production data and present the data analysis and mining results in a visual manner; The data management module is used to store various data in the sulfuric acid production process, quickly query required data according to conditions, and maintain and securely manage the database.

2. A sulfuric acid material balance system according to claim 1, characterized in that: Various sensors and instruments at the sulfuric acid production site collect data from the production process in real time, including material flow, concentration, temperature, and pressure. It also supports manual input or importing historical data and experimental data from other systems.

3. A sulfuric acid material balance system according to claim 1, characterized in that: The balance calculation module is used to store the calculated material balance results in a database, and store the results according to time and production batch.

4. A sulfuric acid material balance system according to claim 1, characterized in that: The model building module uses a mathematical modeling method to construct a sulfuric acid production material balance data model based on the results of process flow analysis and parameter determination. The model describes the material balance relationship of each production link in the form of an equation group, and uses historical production data to verify the constructed model. The model calculation results are compared with actual production data to evaluate the accuracy of the model. The model is optimized according to the verification results and the model parameters or structure are adjusted.

5. A sulfuric acid material balance system according to claim 1, characterized in that: The analysis and mining module is used to display data analysis and mining results in the form of a flowchart. The flowchart includes several points, and different points represent different meanings. Clicking each point can view the corresponding theoretical reference value or actual production value. When a problem occurs in the equipment, the point mark is displayed in orange and red.

6. A sulfuric acid material balance system according to claim 1, characterized in that: The data management module is used to store various types of data in the sulfuric acid production process, including material data, production process data, equipment operation data, and material balance calculation results. It adopts a relational database or a non-relational database and selects a suitable storage method according to the data characteristics and application requirements. It adopts a relational database or a non-relational database and selects a storage method according to the data characteristics and application requirements.

7. A sulfuric acid material balance system according to claim 1, characterized in that: The system also includes an abnormality reminder module, which is used to send a reminder signal in time when abnormal data or equipment failure is detected.

8. A sulfuric acid material balance system according to claim 1, characterized in that: The model building module calls the historical data collected by the acquisition submodule, or the historical data input or imported, and builds a verification threshold adjustment model through a machine learning algorithm. The model is trained using the sorted historical data, and the data is divided into a training set and a test set according to a certain ratio. During the training process, the production season and raw material batch are used as input features, and a reasonable data verification range is used as an output label. By continuously adjusting the parameters of the model, the model learns the relationship between different production conditions and the data verification range.

9. A sulfuric acid material balance system according to claim 1, characterized in that: The check submodule in the balance calculation module uses the dynamically adjusted check threshold to check the collected data. The check rules include data range check, data change trend check, and data correlation check.

10. A sulfuric acid material balance method, characterized in that: A sulfuric acid material balance system according to any one of claims 1 to 9 is used.

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