Real-time online soft measurement method for phosphorus yield of yellow phosphorus furnace

Abstract

The invention belongs to the technical field of yellow phosphorus production, and discloses a real-time online soft measurement method for the phosphorus yield of a yellow phosphorus furnace, which comprises the following steps of: 1, establishing an offline soft instrument model; the CO content X1 and the CO content X2 of the tail gas, the tail gas volume flow V and the refined phosphorus flow are collected; 1.2, data processing: processing the input data, and establishing a relationship between the input data and the output data; and 2, placing the soft instrument model into a system supporting real-time calculation for operation to form an on-line soft instrument, and realizing on-line operation and calibration. By means of the gas component analyzer and the gas volume flow meter, the CO content, the CO content and the flow of the tail gas can be obtained in real time, a soft instrument model is established to quickly calculate the phosphorus yield, the timeliness of measuring the phosphorus yield of the yellow phosphorus furnace is greatly improved, the product quality is stabilized, and the production efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of yellow phosphorus production, and in particular relates to a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace. Background Art

[0002] In the field of yellow phosphorus production, achieving efficient and accurate production process monitoring and control plays a decisive role in ensuring product quality, improving production efficiency and reducing production costs. Figure 1 As shown, the main process for yellow phosphorus production involves adding a mixture of raw materials, including phosphate rock, silica, and white coal, from the raw material process, to an electric arc furnace, where a reduction reaction occurs at high temperatures of 1300-1500°C. During this process, the carbon in the coke reduces the calcium phosphate in the phosphate rock to elemental phosphorus, producing carbon monoxide gas. Due to sealing issues, a small amount of air enters the system, oxidizing some of the carbon monoxide to carbon dioxide. After the furnace exhaust is cooled in a scrubber, the elemental phosphorus enters the phosphorus receiving tank, and the exhaust, rich in CO and H2, enters the subsequent combustion stage.

[0003] However, yellow phosphorus production currently faces a difficult problem: the phosphorus production in the exhaust gas at the reactor outlet is difficult to measure directly with the help of online instruments. The traditional measurement method currently used mainly relies on manual observation of the liquid level changes in the product tank to estimate the daily phosphorus production. This method has many disadvantages. On the one hand, manual observation requires manpower and time, and the liquid level measurement is easily interfered with by external factors, which greatly reduces the accuracy. On the other hand, this method can only obtain daily cumulative phosphorus production data, and cannot provide real-time feedback on the phosphorus production dynamics during the production process, resulting in a serious lag in the measurement results and an inability to provide effective support for the timely adjustment of the production process. This makes it difficult to grasp the changes in phosphorus production in real time during the production process, and thus it is impossible to optimize the production process parameters in a timely and efficient manner, which greatly hinders the development of the yellow phosphorus production industry towards intelligence and efficiency. Therefore, it is urgent to develop a method that can accurately measure the phosphorus production of yellow phosphorus furnaces in real time, online, and in real time. This will fill the technical gaps in the industry and help the yellow phosphorus production industry achieve leapfrog development. Summary of the Invention

[0004] The present invention aims to provide a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace to solve the above technical problems.

[0005] In order to solve the above technical problems, the specific technical solution of a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace of the present invention is as follows: A real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace comprises the following steps: Step 1: establishing an offline soft instrument model; Step 1.1: Data collection: Collect tail gas CO content X1, CO2 content X2, tail gas volume flow V and refined phosphorus flow; Step 1.2: Data processing: Process the input data and establish the relationship between input data and output data; Step 2: Place the soft instrument model into a system that supports real-time computing to form an online soft instrument and realize online operation and calibration.

[0006] Furthermore, the step 1.1 includes the following steps: Step 1.1.1: Install a gas composition analyzer at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas CO content X1 and CO2 content X2; Step 1.1.2: Install a gas volume flow meter at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas volume flow rate V; Step 1.1.3: Install a mass flow meter at the outlet of the phosphorus pump to measure the refined phosphorus flow rate.

[0007] Furthermore, the step 1.2 includes the following steps: Step 1.2.1: The inputs to the soft instrument model include the tail gas CO content X1, CO2 content X2, tail gas volume flow rate V, gas density ρ, proportionality coefficient K, and correction factor A. The output is phosphorus production Fp. Using the tail gas CO content X1, CO2 content X2, and tail gas volume flow rate V, calculate the sum of the CO and CO2 flow rates Fc in the reaction tail gas: Fc = (X1 + X2) * V * ρ. Phosphorus production Fp = K * Fc + A. Step 1.2.2: Timing correction: Based on the time delay between the refined phosphorus flow rate and the CO content, CO2 content, and tail gas volume flow rate, the data is time-corrected; Step 1.2.3: Establish a soft instrument model using the correlation between the sum of the CO and CO2 flow rates Fc and the phosphorus production Fp in the exhaust gas at the yellow phosphorus furnace reaction outlet.

[0008] Furthermore, the timing correction in step 1.2.2 includes the following steps: Step 1.2.3.1: Collect time series data of refined phosphorus flow, CO content, CO2 content, and tail gas volume flow; Step 1.2.3.2: Use lag time and steady-state time parameters to perform dynamic time warping on the time series data of CO content, CO2 content, and exhaust volume flow rate; Step 1.2.3.3: Match the time series data of refined phosphorus flow with the time series data of regularized CO content, CO2 content, and exhaust gas volume flow.

[0009] Furthermore, the establishment of the soft instrument model in step 1.2.3 includes: mechanism modeling, regression analysis, state estimation, pattern recognition, artificial neural network, fuzzy mathematics and support vector machine.

[0010] Furthermore, the step 1.2.3 uses a data regression method to establish a soft instrument model as an example: by collecting refined phosphorus flow data, collecting its corresponding tail gas CO content X1, CO2 content X2, and tail gas volume flow V for data regression, and obtaining the proportional coefficient K and correction factor A; specifically comprising the following steps: Step 1.2.3.1: Define a computation function that takes four input features X1, X2, V, ρ and an output target Fp as parameters. Step 1.2.3.2: Calculate the intermediate variables and the sum of the CO and CO2 flow rates in the exhaust gas: Fc = (X1 + X2) * V * ρ; Step 1.2.3.3: Create and fit a linear regression model, find the optimal linear relationship based on the input feature Fc and the target value Fp, and determine the values of K and A so that the error between the model prediction value and the actual value is minimized; Step 1.2.3.4: Obtain the coefficients of the linear regression model, which is the value of K; Step 1.2.3.5: Obtain the intercept of the linear regression model, which is the value of A.

[0011] Furthermore, the step 2 includes the following steps: Step 2.1: Configuration and online operation; including a filtering processing module, wherein the filtering processing module performs filtering processing on the original signals of exhaust CO content, CO2 content and exhaust volume flow; Step 2.2: Online correction: by adjusting the proportional coefficient K and the correction factor A, the soft instrument model is corrected in real time.

[0012] The present invention provides a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace, which has the following advantages: The method significantly improves the timeliness of phosphorus production measurement in a yellow phosphorus furnace: Using a gas composition analyzer and a gas volume flowmeter, the CO content, CO2 content, and flow rate of the tail gas can be obtained in real time. A soft instrument model is established to quickly calculate phosphorus production, breaking the delay limitations of traditional manual liquid level observation, allowing production personnel to monitor phosphorus production dynamics at any time. Accuracy is significantly improved: Based on reaction equilibrium calculations such as reduction reactions and carbonates, the problem of traditional liquid level measurement being subject to external interference is avoided. Through precise gas analysis and rigorous chemical calculations, phosphorus production data is more reliable. The method effectively optimizes production processes: With real-time and accurate phosphorus production data, operators can promptly optimize process parameters, ensure optimal production, stabilize product quality, and improve production efficiency. The soft instrument model, with its offline modeling and online adjustment, significantly improves computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a simplified process flow diagram of the yellow phosphorus electric furnace system. DETAILED DESCRIPTION

[0014] In order to better understand the purpose, structure and function of the present invention, the following is a further detailed description of a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace of the present invention in conjunction with the accompanying drawings.

[0015] like Figure 1 As shown, a real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace of the present invention comprises the following steps: Step 1: Establish an offline soft instrument model; Step 1.1: Data collection: Collect tail gas CO content X1, CO2 content X2, tail gas volume flow V and refined phosphorus flow; Step 1.1.1: Install a gas composition analyzer at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas CO content X1 and CO2 content X2; Step 1.1.2: Install a gas volume flow meter at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas volume flow rate V; Step 1.1.3: Install a mass flow meter at the outlet of the phosphorus pump to measure the refined phosphorus flow rate; Step 1.2: Data processing: Process the input data and establish the relationship between input data and output data; Step 1.2.1: The inputs to the soft instrument model include the tail gas CO content X1, CO2 content X2, tail gas volume flow rate V, gas density ρ, proportionality coefficient K, and correction factor A. The output is phosphorus production Fp. Using the tail gas CO content X1, CO2 content X2, and tail gas volume flow rate V, calculate the sum of the CO and CO2 flow rates Fc in the reaction tail gas: Fc = (X1 + X2) * V * ρ; phosphorus production Fp = K * Fc + A. Step 1.2.2: Timing correction: Since there is a certain time delay between the refined phosphorus flow rate and the CO content, CO2 content, and tail gas volume flow rate, the data needs to be time-corrected; Timing correction method: Step 1.2.3.1: Collect time series data of refined phosphorus flow, CO content, CO2 content, and tail gas volume flow; Step 1.2.3.2: Use lag time and steady-state time parameters to perform dynamic time warping on the time series data of CO content, CO2 content, and exhaust volume flow rate; Step 1.2.3.3: Match the time series data of refined phosphorus flow with the regularized time series data of CO content, CO2 content, and tail gas volume flow; Step 1.2.3: Build a soft instrument model using the correlation between the sum of the CO and CO2 flow rates Fc and the phosphorus production Fp in the tail gas at the yellow phosphorus furnace reactor outlet. There are various methods for building soft instrument models, including mechanism modeling, regression analysis, state estimation, pattern recognition, artificial neural networks, fuzzy mathematics, and support vector machines. For example, building a soft instrument model using data regression involves collecting refined phosphorus flow data, its corresponding tail gas CO content X1, CO2 content X2, and tail gas volume flow rate V, and performing data regression to obtain the proportionality coefficient K and correction factor A. Step 1.2.3.1: Define a computation function that takes four input features (X1, X2, V, ρ) and an output target (Fp) as parameters. Step 1.2.3.2: Calculate the intermediate variables and the sum of the CO and CO2 flow rates in the exhaust gas: Fc = (X1 + X2) * V * ρ; Step 1.2.3.3: Create and fit a linear regression model, find the optimal linear relationship based on the input feature Fc and the target value Fp, and determine the values of K and A so that the error between the model prediction value and the actual value is minimized; Step 1.2.3.4: Obtain the coefficients of the linear regression model, which is the value of K; Step 1.2.3.5: Obtain the intercept of the linear regression model, which is the value of A.

[0016] Step 2: Run the soft instrument model in a system that supports real-time computing, forming an online soft instrument and enabling online operation and calibration. After establishing the soft instrument model, the soft instrument can be implemented in any system that supports real-time computing, including but not limited to single-chip microcomputers, PLC systems, DCS systems, and real-time database systems. It can generally be implemented in the factory's existing PLC or DCS system. Step 2.1: Configuration and online operation; including a filtering processing module to filter the raw signals of exhaust CO content, CO2 content and exhaust volume flow; Step 2.2: Online correction: The soft instrument model can be corrected in real time by adjusting the proportional coefficient K and the correction factor A.

[0017] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A real-time online soft measurement method for phosphorus production in a yellow phosphorus furnace, characterized in that: The steps include: Step 1: Establish an offline soft instrument model; Step 1.1: Data collection: Collect tail gas CO content X1, CO2 content X2, tail gas volume flow V and refined phosphorus flow; Step 1.2: Data processing: Process the input data and establish the relationship between input data and output data; Step 2: Place the soft instrument model into a system that supports real-time computing to form an online soft instrument and realize online operation and calibration.

2. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 1, wherein The step 1.1 includes the following steps: Step 1.1.1: Install a gas composition analyzer at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas CO content X1 and CO2 content X2; Step 1.1.2: Install a gas volume flow meter at the exhaust gas duct at the reactor outlet or at the compressor inlet to measure the exhaust gas volume flow rate V; Step 1.1.3: Install a mass flow meter at the outlet of the phosphorus pump to measure the refined phosphorus flow rate.

3. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 1, wherein The step 1.2 includes the following steps: Step 1.2.1: The inputs of the soft instrument model include the tail gas CO content X1, CO2 content X2, tail gas volume flow rate V, gas density ρ, proportionality coefficient K, and correction factor A. The output is phosphorus production Fp. Using the tail gas CO content X1, CO2 content X2, and tail gas volume flow rate V, calculate the sum of the CO and CO2 flow rates Fc in the reaction tail gas: Fc = (X1 + X2) * V * ρ; phosphorus production Fp = K * Fc + A. Step 1.2.2: Timing correction: Based on the time delay between the refined phosphorus flow rate and the CO content, CO2 content, and tail gas volume flow rate, the data is time-corrected; Step 1.2.3: Establish a soft instrument model using the correlation between the sum of the CO and CO2 flow rates Fc and the phosphorus production Fp in the exhaust gas at the yellow phosphorus furnace reaction outlet.

4. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 3, wherein The timing correction of step 1.2.2 includes the following steps: Step 1.2.3.1: Collect time series data of refined phosphorus flow, CO content, CO2 content, and tail gas volume flow; Step 1.2.3.2: Use lag time and steady-state time parameters to perform dynamic time warping on the time series data of CO content, CO2 content, and exhaust volume flow rate; Step 1.2.3.3: Match the time series data of refined phosphorus flow with the time series data of regularized CO content, CO2 content, and exhaust gas volume flow.

5. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 3, wherein The establishment of the soft instrument model in step 1.2.3 includes: mechanism modeling, regression analysis, state estimation, pattern recognition, artificial neural network, fuzzy mathematics and support vector machine.

6. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 3, characterized in that: The step 1.2.3 uses the data regression method to establish a soft instrument model as an example: by collecting refined phosphorus flow data, collecting its corresponding tail gas CO content X1, CO2 content X2, and tail gas volume flow V for data regression, and obtaining the proportional coefficient K and correction factor A; specifically, the steps include: Step 1.2.3.1: Define a computation function that takes four input features X1, X2, V, ρ and an output target Fp as parameters. Step 1.2.3.2: Calculate the intermediate variables and calculate the sum of the CO and CO2 flow rates in the exhaust gas: Fc = (X1 + X2) * V * ρ; Step 1.2.3.3: Create and fit a linear regression model, find the optimal linear relationship based on the input feature Fc and the target value Fp, and determine the values of K and A so that the error between the model prediction value and the actual value is minimized; Step 1.2.3.4: Obtain the coefficients of the linear regression model, which is the value of K; Step 1.2.3.5: Obtain the intercept of the linear regression model, which is the value of A.

7. The real-time online soft measurement method for yellow phosphorus furnace phosphorus production according to claim 3, wherein: The step 2 comprises the following steps: Step 2.1: Configuration and online operation; including a filtering processing module, wherein the filtering processing module performs filtering processing on the original signals of exhaust CO content, CO2 content and exhaust volume flow; Step 2.2: Online correction: by adjusting the proportional coefficient K and the correction factor A, the soft instrument model is corrected in real time.

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