Titanium dioxide calcination control method, device and system

Abstract

The invention provides a titanium dioxide calcination control method, device and system, and relates to the technical field of automatic control, and the control method comprises the steps: obtaining the current batch production information of to-be-produced titanium dioxide and the corresponding preset batch quality information; inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy; and controlling the rotary kiln system according to the final control strategy. Through accurate data analysis and processing, the method can optimize the calcination process, so that the final quality of titanium dioxide is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of automatic control technology, and in particular to a control method, device and system for calcining titanium dioxide. Background Art

[0002] Titanium dioxide is a high-performance pigment widely used in industries such as coatings, papermaking, chemicals, electronics, ceramics, pharmaceuticals and food additives. Calcination is a key step in its production process, which involves placing the hydrolyzed titanium dioxide filter cake into a tilted rotating kiln, and converting the filter cake into titanium dioxide through high-temperature dehydration and calcination. This process can produce two crystal forms, anatase and rutile, of which rutile usually exhibits better performance than anatase.

[0003] During the calcination process, anatase gradually transforms into rutile at a high temperature of about 700°C, accompanied by energy release. This transformation process is not completed instantly, but is gradual and irreversible. Therefore, the calcination process has a significant impact on product quality. However, in the current titanium dioxide production process, the accuracy of rotary kiln calcination temperature control technology is poor, resulting in low product quality stability, possible excess or substandard phenomena, and also causing energy waste. Summary of the invention

[0004] The problem solved by the present disclosure is how to accurately control the titanium dioxide calcination process to control the product quality stability and improve production efficiency.

[0005] In order to solve the above problems, the present disclosure provides a control method, device and system for calcining titanium dioxide.

[0006] In a first aspect, the present disclosure provides a method for controlling the calcination of titanium dioxide, which is applied to a rotary kiln system for producing titanium dioxide. The method for controlling the calcination of titanium dioxide comprises:

[0007] Obtaining the current batch production information and the corresponding preset batch quality information of the titanium dioxide to be produced;

[0008] Inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy;

[0009] The rotary kiln system is controlled according to the final control strategy.

[0010] Optionally, the calcination control model includes a process dynamic module, and the current batch production information and the corresponding preset batch quality information are input into the calcination control model to obtain an optimal control strategy, including:

[0011] Predicting the current batch production information through the process dynamic module to obtain temporary predicted quality information within a future preset period;

[0012] The optimal control strategy is obtained according to the temporary predicted quality information and the preset batch quality information.

[0013] Optionally, obtaining the optimal control strategy according to the temporary predicted quality information and the preset batch quality information includes:

[0014] Get the preset optimization objective function;

[0015] Obtaining deviation data according to the temporary predicted quality information and the preset batch quality information;

[0016] Based on the preset optimization objective function, the optimal control strategy is obtained according to the deviation data.

[0017] Optionally, after controlling the rotary kiln system according to the optimal control strategy, the method further includes:

[0018] Obtaining the current actual batch quality information of the titanium dioxide to be produced;

[0019] Correcting the temporary predicted quality information based on the current actual batch quality information to obtain corrected temporary predicted quality information;

[0020] The current optimal control strategy is obtained according to the corrected temporary predicted quality information and the preset batch quality information.

[0021] Optionally, the current batch production information includes flame value, hot air value, feed amount and kiln temperature, and the temporary predicted quality information includes predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced; the current batch production information is predicted by the process dynamic module to obtain the temporary predicted quality information within a preset time period in the future, including:

[0022] The flame value, the hot air value, the feed amount and the temperature in the kiln are comprehensively processed through the process dynamic module to obtain the predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced in a future preset time period.

[0023] Optionally, the method for controlling the calcination of titanium dioxide further comprises:

[0024] After controlling the rotary kiln system according to the final control strategy, obtaining current state information of the rotary kiln system;

[0025] Based on the model predictive control algorithm, optimization processing is performed according to the current state information to obtain an actual control strategy, and the rotary kiln system is controlled according to the actual control strategy.

[0026] Optionally, the model predictive control algorithm includes:

[0027]

[0028] Among them, J is the cost function, is the transpose of the state error, which represents the difference between the current state information of the system and the corresponding predicted state at time step k. is the transpose of the control input, which represents the actual control strategy applied to the system at time step k. Q and R are weight matrices, respectively. is the transpose of the terminal state error, F is the terminal cost matrix, and m is the length of the prediction time domain.

[0029] Optionally, the rotary kiln system includes a rotary kiln, the current state information includes temperature information; and obtaining the current state information includes:

[0030] Acquire multiple temperature positions on the axial center line of the kiln chamber of the rotary kiln;

[0031] For one temperature position, weighted processing is performed on multiple temporary temperature points corresponding to the temperature position to obtain corresponding temperature data, and the temperature information is obtained based on the temperature data of all the temperature positions, wherein the temporary temperature point is located in a preset range on a plane that includes the corresponding temperature position and is perpendicular to the axial center line.

[0032] In a second aspect, the present disclosure provides a control device for calcining titanium dioxide, which is applied to a rotary kiln system for generating titanium dioxide. The control device for calcining titanium dioxide includes:

[0033] An acquisition unit, used to acquire current batch production information of the titanium dioxide to be produced and corresponding preset batch quality information;

[0034] The regulating unit enters the calcination control model processing process: the current batch production information and the corresponding preset batch quality information are input into the calcination control model to obtain a final control strategy; and the rotary kiln system is controlled according to the final control strategy.

[0035] In a third aspect, the present disclosure provides a control system for calcining titanium dioxide, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the control method for calcining titanium dioxide as described in the first aspect when executing the computer program.

[0036] The beneficial effects of the control method, device and system for calcining titanium dioxide disclosed in the present invention are:

[0037] The titanium dioxide calcination control method aims to optimize the calcination process of the rotary kiln and improve the quality of the final product through precise data analysis and processing.

[0038] Before calcination begins, the production information of the current batch will be obtained first, including parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc. At the same time, the corresponding preset batch quality information is collected, which often includes whiteness, gloss, hiding power, etc.

[0039] The current batch production information and the preset batch quality information are input into the trained calcination control model. This model can be built based on machine learning algorithms, mathematical models or other data analysis tools to analyze the relationship between the two.

[0040] The model processes this information and uses basic data and algorithms to calculate the optimal control parameters and strategies to ensure that temperature, time and other key indicators in the production process can meet the expected quality standards.

[0041] According to the final control strategy output by the model, the operating conditions of the rotary kiln system are adjusted. This may include adjusting the flame intensity, air flow velocity, kiln tilt angle, etc. to ensure the efficiency and stability of the calcination process.

[0042] It can be seen that through precise data analysis and processing, this method can optimize the calcination process and thus improve the final quality of titanium dioxide. Specifically, it can ensure that the product meets the expected standards in key quality indicators such as whiteness, gloss, and hiding power. And before calcination, by obtaining the production information of the current batch (such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc.), comprehensive monitoring of the production process is achieved. This real-time data acquisition and analysis can timely discover and correct deviations to ensure stable production. Using a trained calcination control model (based on machine learning or other data analysis tools), the relationship between current production information and preset quality information can be deeply analyzed, and the optimal control parameters and strategies can be intelligently calculated. This intelligent control method improves the flexibility and adaptability of production; according to the control strategy output by the model, the operating conditions of the rotary kiln (such as flame intensity, air flow velocity, kiln body inclination angle, etc.) can be accurately adjusted to ensure the efficiency and stability of the calcination process. This refined control helps to reduce energy consumption and improve production efficiency.

[0043] Therefore, the present invention significantly improves production efficiency and product quality through data-driven intelligent control, and has important economic and technical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1It is a schematic flow chart of a method for controlling the calcination of titanium dioxide according to an embodiment of the present disclosure;

[0045] Figure 2 It is a schematic diagram of a kiln chamber of a rotary kiln according to an embodiment of the present disclosure;

[0046] Figure 3 A schematic diagram of control optimization of a titanium dioxide rotary kiln according to an embodiment of the present disclosure;

[0047] Figure 4 It is a linear fitting schematic diagram of each controlled variable in the embodiment of the present disclosure;

[0048] Figure 5 It is a flow chart of an advanced control path of a rotary kiln system according to an embodiment of the present disclosure;

[0049] Figure 6 This is a schematic structural diagram of a control device for calcining titanium dioxide according to an embodiment of the present disclosure;

[0050] Figure 7 It is a structural schematic diagram of a control system for calcining titanium dioxide according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0052] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0053] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0054] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0055] In the prior art, calcination is a key step in the production process of titanium dioxide. Its working principle is: the filtered titanium dioxide hydrolyzate filter cake is input into the tail end (high end of the cylinder) of the rotary kiln body with a certain slope and slowly rotating, and at the same time, the combustion mixing chamber set in the direction of the kiln head (low end of the cylinder) blows clean flames and hot air into the kiln to promote the dehydration and calcination of the filter cake. The hydrolyzed titanium dioxide filter cake after high-temperature dehydration and calcination, with the help of the inclined rotary kiln body, rolls in the circumferential direction and moves in the axial direction (from the high end to the low end), thereby continuously completing the dehydration and calcination process. Finally, the material enters the cooler through the double hydraulic flap discharge valve at the bottom of the kiln head cover, and the material after cooling and screening is the finished titanium dioxide product.

[0056] Two main crystal forms are usually produced in this process: anatase and rutile. The pigment properties of rutile titanium dioxide are usually better than those of anatase. In addition, factors such as crystal particle diameter, growth rate and particle size distribution also have a significant impact on the quality of the final product. Anatase titanium dioxide can be converted into rutile under high temperature conditions (the minimum conversion temperature is about 700°C). This conversion process is accompanied by energy release of about 7.5×4.1868kJ / mol. This conversion process is not completed instantly, but is gradual and irreversible.

[0057] For titanium dioxide manufacturers, producing products that meet different quality standards requires extremely precise calcination control technology. Slight deviations in the calcination process may result in substandard or excessive product quality, thus affecting the company's economic benefits. Therefore, companies must take more precise control measures to ensure the stability and consistency of product quality, thereby improving production efficiency and energy utilization.

[0058] And in the calcination process, the following key issues are involved: 1. Which variables to adjust, and when to adjust should be determined based on production needs and real-time monitoring data, and the adjustment range should be controlled within a reasonable range. 2. Monitor the adjustment range: A monitoring mechanism needs to be established to ensure that the amplitude of the adjusted variables is not too large. By setting a reasonable adjustment threshold, when a variable changes beyond this range, an alarm should be automatically triggered or further adjustments should be restricted. 3. Monitor the adjustment frequency: The frequency of adjustment should be monitored to ensure that the adjustment of variables is not too frequent.

[0059] In view of the problems existing in the above-mentioned related technologies, this embodiment provides a control method, device and system for calcining titanium dioxide.

[0060] like Figure 1 As shown, a control method for calcining titanium dioxide provided by an embodiment of the present disclosure is applied to a rotary kiln system for producing titanium dioxide. The control method for calcining titanium dioxide includes:

[0061] Step S100, obtaining the current batch production information of the titanium dioxide to be produced and the corresponding preset batch quality information.

[0062] Specifically, the current batch production information of the titanium dioxide to be produced is obtained during the actual production process, such as parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc. The corresponding preset batch quality information is also obtained, which can be obtained through historical production data, quality standards, product specification parameters, and customer needs. This information can be extracted from the quality management system to help determine the quality requirements that need to be achieved for the production of this batch.

[0063] At the same time, the current batch production information can be compared with the preset batch quality information to evaluate whether the current production conditions meet the product quality requirements. The comparison content may include product particle size, color, brightness, chemical composition, etc.

[0064] Based on the information collected and analyzed above, a data basis is provided for subsequent processes.

[0065] Step S200, inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy.

[0066] Specifically, the current batch production information (including raw material characteristics, production parameters, etc.) acquired in step S100 and the preset batch quality information (including quality standards, etc.) are integrated into a unified data format for easy input into the control model.

[0067] The integrated data is reviewed and cleaned to ensure its accuracy and completeness and to eliminate any possible errors or outliers.

[0068] Select an applicable calcination control model: Select an appropriate model based on production requirements, which may include models based on physical and chemical processes, machine learning models, or empirical control models.

[0069] According to the specific conditions and historical data of the current batch, the parameters of the model are initialized, such as temperature control range, rotation speed, air flow, etc., to ensure that the model can accurately reflect the actual production process.

[0070] Enter the calcination control model processing process: input the integrated current batch production information and preset batch quality information into the control model. The model calculates based on the input data (flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature and other parameters), analyzes the relationship between production conditions and preset quality standards in real time, and optimizes various calcination parameters, including temperature curve, calcination time and atmosphere conditions.

[0071] Based on the analysis results, the model generates the final control strategy for the current batch. The strategy includes specific operational instructions required to achieve the expected quality standards, such as the actual control of parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc. The generated control strategy is fed back to the production system, and real-time monitoring is carried out and compared with the actual production situation to verify the effectiveness of the control strategy, and dynamic adjustments can be made if necessary.

[0072] By inputting precise current production information and preset quality standards, the control model can provide very accurate control strategies for the calcination process, thereby improving the stability and consistency of the production process.

[0073] And the production information includes parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc., that is, multiple inputs, and the final control strategy of the output, that is, the process formula, includes the actual control strategy for parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc., that is, a control algorithm with multiple inputs and multiple outputs is realized based on the calcination control model, which can solve the problems of mutual interference and control limitations, and adjust the calcination conditions inside the rotary kiln according to the model.

[0074] The control model can process input information and adjust production parameters in real time, allowing enterprises to quickly respond to changes in production, make timely adjustments, and reduce downtime. The data-driven calcination control model introduces more intelligent elements, promotes the transformation of traditional manufacturing to intelligent manufacturing, and helps enterprises achieve higher levels of digitalization and automation in the future. The automated control and decision-making process reduces the need for manual intervention, thereby reducing labor costs and operational errors caused by human factors.

[0075] In summary, step S200 not only optimizes the calcination process of titanium dioxide production process through efficient data input and precise control model application, but also brings higher benefits and more stable product quality to the enterprise. This process has important strategic significance in improving production efficiency and economic benefits.

[0076] Step S300: controlling the rotary kiln system according to the final control strategy.

[0077] Specifically, the final control strategy is applied to the rotary kiln system to optimize the calcination process of titanium dioxide. According to the final control strategy generated in step S200, necessary control parameters are extracted, such as temperature setting (actual control of parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc.), heating time, air flow rate, material feeding rate, etc.

[0078] Convert the control strategy into signals suitable for the operation of the rotary kiln system, and ensure that the control information can be received and executed by the actual equipment. This involves converting digital signals into analog signals, or sending them directly to the equipment through a programmable logic controller (PLC).

[0079] The operating data of the rotary kiln is collected in real time through sensors and monitoring systems, such as key parameters such as temperature, gas composition, material flow and rotation speed. And according to the control signal, the operation of the rotary kiln is adjusted. This may include: controlling the combustion system to achieve the required calcination temperature. Adjusting the rate and sequence of raw material delivery to ensure the reaction under optimal conditions. Managing the atmosphere in the kiln to ensure the calcination effect and prevent excessive pollutant emissions.

[0080] At the same time, operating data can be continuously collected and compared with the set targets. Any deviation from the set value will be recorded and fed back. If there is a significant difference between the real-time data and the preset indicators, the control system will be able to adjust the operation to meet the target. This feedback mechanism enables the rotary kiln to continuously optimize operating parameters to ensure optimal production conditions. At the same time, a monitoring system can also be set up to detect any abnormal conditions (such as excessive temperature or equipment failure) and trigger an automatic alarm system to respond quickly and take necessary safety measures. The real-time monitoring and fault alarm system improves production safety and effectively prevents potential safety hazards and environmental accidents. And based on the feedback mechanism of real-time data, the production system can quickly respond to changes in production demand or fluctuations in raw material characteristics, thereby improving production flexibility.

[0081] Through the effective application of control strategies, the operating efficiency of the rotary kiln is significantly improved, achieving more uniform heat treatment and shorter production cycles, increasing output. The temperature and reaction conditions during the calcination process are strictly controlled so that the final product can always meet the preset quality standards, reducing the risk of defective products. At the same time, precise control reduces energy consumption, and the optimized production process reduces the waste of raw materials and energy, thereby reducing production costs.

[0082] In this embodiment, the titanium dioxide calcination control method aims to optimize the calcination process of the rotary kiln and improve the quality of the final product through precise data analysis and processing.

[0083] Before calcination begins, the production information of the current batch will be obtained first, including parameters such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc. At the same time, the corresponding preset batch quality information is collected, that is, the quality expected to be achieved, generally including whiteness, gloss, hiding power, etc.

[0084] The current batch production information and the preset batch quality information are input into the trained calcination control model. This model can be built based on machine learning algorithms, mathematical models or other data analysis tools to analyze the relationship between the two.

[0085] The model processes this information and uses basic data and algorithms to calculate the optimal control parameters and strategies to ensure that temperature, time and other key indicators in the production process can meet the expected quality standards.

[0086] According to the final control strategy output by the model, the operating conditions of the rotary kiln system are adjusted. This may include adjusting the flame intensity, air flow velocity, kiln tilt angle, etc. to ensure the efficiency and stability of the calcination process.

[0087] It can be seen that through precise data analysis and processing, this method can optimize the calcination process and thus improve the final quality of titanium dioxide. Specifically, it can ensure that the product meets the expected standards in key quality indicators such as whiteness, gloss, and hiding power. And before calcination, by obtaining the production information of the current batch (such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature, etc.), comprehensive monitoring of the production process is achieved. This real-time data acquisition and analysis can timely discover and correct deviations to ensure stable production. Using a trained calcination control model (based on machine learning or other data analysis tools), the relationship between current production information and preset quality information can be deeply analyzed, and the optimal control parameters and strategies can be intelligently calculated. This intelligent control method improves the flexibility and adaptability of production; according to the control strategy output by the model, the operating conditions of the rotary kiln (such as flame intensity, air flow velocity, kiln body inclination angle, etc.) can be accurately adjusted to ensure the efficiency and stability of the calcination process. This refined control helps to reduce energy consumption and improve production efficiency.

[0088] Optionally, the calcination control model includes a process dynamic module, and the current batch production information and the corresponding preset batch quality information are input into the calcination control model to obtain an optimal control strategy, including:

[0089] Predicting the current batch production information through the process dynamic module to obtain temporary predicted quality information within a future preset period;

[0090] The optimal control strategy is obtained according to the temporary predicted quality information and the preset batch quality information.

[0091] Optionally, the current batch production information includes flame value, hot air value, feed amount and kiln temperature, and the temporary predicted quality information includes predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced; the current batch production information is predicted by the process dynamic module to obtain the temporary predicted quality information within a preset time period in the future, including:

[0092] The flame value, the hot air value, the feed amount and the temperature in the kiln are comprehensively processed through the process dynamic module to obtain the predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced in a future preset time period.

[0093] Specifically, the calcination control model is an integrated dynamic system that aims to improve the quality control and production efficiency of the calcination process. The model consists of process dynamic modules, and its workflow can be divided into the following steps:

[0094] Firstly, the production information of the current batch (such as flame value, hot air value, feed amount and kiln temperature, etc.) and the corresponding preset batch quality information (including standard quality parameters) are input into the calcination control model.

[0095] The process dynamics module uses various prediction algorithms and dynamic models to make real-time predictions on the input current batch production information and generate temporary predicted quality information. This prediction is based on the analysis of historical data, process variables and their interrelationships, and can take into account multiple factors such as temperature changes, airflow rate, raw material characteristics, etc., reflecting the quality trend of the current batch.

[0096] By comparing the temporary predicted quality information with the preset batch quality information, the model identifies the possible quality deviation of the current batch. Based on this information, the optimal control strategy is generated using certain optimization algorithms (such as linear programming, nonlinear optimization, genetic algorithms, etc.). This strategy includes necessary adjustment measures, such as changing the calcination temperature and adjusting the specific instructions for the raw material feeding rate to ensure that the quality of the final product meets or exceeds the preset standards.

[0097] Through dynamic prediction and real-time adjustment, we can ensure that the quality of the current batch of products can meet or exceed the preset standards and reduce the production of unqualified products. In addition, during the production process, we can quickly respond to changes in raw material characteristics or operating conditions and make corresponding adjustments in time, thereby enhancing the flexibility of the production process.

[0098] The generation of optimal control strategies makes the use of various resources (such as energy and raw materials) more efficient, reduces production costs, and increases output. Accurate prediction and control reduce rework, scrap and inventory loss caused by quality problems, thereby significantly reducing economic losses in the production process.

[0099] In summary, the calcination control model plays a vital role in improving quality control and production efficiency, which not only brings economic benefits to the enterprise, but also improves product competitiveness.

[0100] Optionally, obtaining the optimal control strategy according to the temporary predicted quality information and the preset batch quality information includes:

[0101] Get the preset optimization objective function;

[0102] Obtaining deviation data according to the temporary predicted quality information and the preset batch quality information;

[0103] Based on the preset optimization objective function, the optimal control strategy is obtained according to the deviation data.

[0104] Specifically, this process is a rolling optimization process. First, the optimization objective function is clarified, which is usually composed of the company's quality standards, production costs, energy consumption and other goals. For example, the optimization objective function can be set to minimize product quality fluctuations, reduce production costs or reduce energy consumption. These objective functions will serve as the basis for subsequent decision-making and control strategy optimization. It can also be based on controlled quantities, such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature and other parameters as optimal performance indicators. Once the optimization objective function is determined, the deviation data needs to be calculated next. The quality deviation can be calculated by comparing the temporary predicted quality information (i.e., the expected quality index of the current batch) with the preset batch quality information (i.e., the quality standard expected to be achieved). This deviation data is usually manifested as the difference between various quality parameters (such as chemical composition, physical properties, etc.), reflecting the gap between the current production and the preset standard.

[0105] Finally, based on the deviation information just obtained, it is combined with the preset optimization objective function, and the optimal control strategy is calculated using a suitable optimization algorithm (such as linear programming, dynamic programming, genetic algorithm, etc.). These strategies can be specifically reflected in the adjustment plan of key variables in the calcination process (such as temperature, raw material ratio, airflow rate, etc.), ensuring that the objective function is optimized through reasonable control.

[0106] It should be noted that, usually, the relationship between each pair of control variables (flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature and other parameters) and controlled variables is simplified into a linear model. In each control cycle, the calcination control model predicts the values ​​of all controlled variables (flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature and other parameters) and implements optimization control to ensure that all controlled variables remain within the set range. In addition to pressure and flow, performance indicators and constraints (such as flame value, hot air value, upper and lower limits of flame value, upper and lower limits of hot air value) are also set in the system as process parameters. For the titanium dioxide rotary kiln process control object, we carried out overall optimization control and realized the coordinated control of multiple variables.

[0107] By using deviation data for precise adjustment, the quality fluctuations between batches can be effectively reduced, product quality can be kept consistent, and market competitiveness can be enhanced. The setting of the optimization objective function helps enterprises clarify production goals, so as to make more targeted process improvements, improve production efficiency and resource utilization. By optimizing the control strategy, unnecessary raw material losses, energy consumption and production costs can be reduced, and the economic benefits of the enterprise can be indirectly improved. In the production process, the preset optimization objective function is combined with the real-time deviation data, so that the production system can respond to uncertainties such as raw material changes and operating condition adjustments in real time, and enhance the adaptability of the system. The generated optimal control strategy provides data-based decision support, helping management to make more scientific and reasonable adjustments in the production process and promote the development of intelligent manufacturing. At the same time, through the continuous monitoring and evaluation of deviation data, enterprises can continuously optimize the production process, form a virtuous circle, and promote the continuous improvement of the overall process level.

[0108] In summary, the optimal control strategy generated according to the temporary predicted quality information and the preset batch quality information can not only effectively improve product quality and production efficiency, but also help reduce production costs and enhance the overall competitiveness of the enterprise.

[0109] It should be noted that for the deviation between the set value (preset batch quality information) and the predicted value (temporary predicted quality information) of the titanium dioxide batch quality based on the output expectation, the control amount for the current and future time domain can be calculated according to a certain optimization target (such as flame value). The future control effect is determined by optimizing the performance index of the optimization target (such as flame value). In addition, the rolling optimization process is not completed in one offline operation, but is repeatedly performed online.

[0110] Optionally, after controlling the rotary kiln system according to the optimal control strategy, the method further includes:

[0111] Obtaining the current actual batch quality information of the titanium dioxide to be produced;

[0112] Correcting the temporary predicted quality information based on the current actual batch quality information to obtain corrected temporary predicted quality information;

[0113] The current optimal control strategy is obtained according to the corrected temporary predicted quality information and the preset batch quality information.

[0114] Specifically, in order to prevent model (calcination control model) mismatch and cope with various uncertainties and changes faced by titanium dioxide in the actual production site, when a new batch is subjected to the rotary kiln calcination process, it is necessary to follow the reference trajectory proposed by the model to achieve the preset batch quality target value. At the current moment, the actual output batch quality information of the detection object is detected, and this real-time information is used to correct the prediction results based on the model. At the next moment, the control amount is recalculated according to the above steps based on the new measurement data (i.e., a new optimization is performed). The predicted output batch quality information is continuously adjusted according to the actual output batch quality information, so that the rolling optimization is not only based on the model, but is a continuously rolling local optimization process, and feedback information is used to form a closed-loop optimization.

[0115] That is, after the optimal control strategy is implemented, the production process continues, and the quality information of the current actual batch is monitored and obtained in real time. This information includes the physical and chemical properties of the final product, such as the purity, particle size distribution, and color difference of titanium dioxide. By comparing the current actual batch quality information with the previously obtained temporary predicted quality information, the latter is corrected. This step uses data analysis and model adjustment, which may involve weighted averaging, model correction, or machine learning algorithms to improve the accuracy of the prediction.

[0116] The control strategy is updated by using the relationship between the modified temporary predicted quality information and the preset batch quality information (i.e. the expected quality standard) in combination with the current optimization goal. In this way, the new control strategy will be more in line with the actual production situation and the expected goal, providing more effective control guidance for subsequent production.

[0117] By correcting the temporary predicted quality information in real time, the model can significantly improve its grasp of the actual production situation and ensure a more accurate prediction of the quality of future batches. Updating the control strategy based on the latest predicted information means that the control decision can better reflect the current production situation, thereby enhancing the responsiveness of the production process. The corrected prediction information and optimized control strategy help reduce the fluctuation of the quality of each batch, ensure the consistency of product quality, and improve market competitiveness. During the production process, the control strategy can be adjusted in real time according to the actual situation to make the system more flexible to deal with problems such as raw material fluctuations or changes in equipment status. At the same time, through precise control and real-time optimization, the efficiency of the entire production process can be improved, which not only improves resource utilization, but also may reduce production costs, thereby improving overall economic benefits. The repeated online correction process provides a valuable data accumulation and analysis basis, which helps enterprises make scientific decisions in process improvement, equipment upgrades, etc., and promotes continuous optimization.

[0118] In summary, this process not only improves the control accuracy and production efficiency of the rotary kiln system, but also helps ensure that product quality meets expectations and enhances the system's adaptability and responsiveness.

[0119] Optionally, the control of the calcination of titanium dioxide further includes:

[0120] After controlling the rotary kiln system according to the final control strategy, obtaining current state information of the rotary kiln system;

[0121] Based on the model predictive control algorithm, optimization processing is performed according to the current state information to obtain an actual control strategy, and the rotary kiln system is controlled according to the actual control strategy.

[0122] Optionally, the model predictive control algorithm includes:

[0123]

[0124] Among them, J is the cost function, is the transpose of the state error, which represents the difference between the current state information of the system and the corresponding predicted state at time step k. is the transpose of the control input, which represents the actual control strategy applied to the system at time step k. Q and R are weight matrices, respectively. is the transpose of the terminal state error, F is the terminal cost matrix, and m is the length of the prediction time domain.

[0125] Specifically, MPC (Model Predictive Control, MPC) is an advanced control strategy that is widely used in industrial process control, especially in complex systems that need to deal with multiple variables and constraints.

[0126] Collect various parameters of the current production operation (current state information, such as flame value, hot air value, feed amount, kiln temperature, titanium dioxide surface temperature and other parameters). These parameters are a direct reflection of the current state of the system. For the MPC algorithm, understanding the current state is the basis for prediction and control. Perform optimization based on the current state. Use the MPC algorithm formula for optimization calculation. J in the formula is the cost function, which is an indicator to measure system performance, and the goal is to minimize this function.

[0127] E k : State error vector, that is, the difference between the current state information and the corresponding predicted state.

[0128] Q: The weight matrix of the state error, which is used to adjust the importance of the state error in the cost function.

[0129] u k : Control input vector, representing the control action applied to the system at time step k.

[0130] R: The weight matrix of the control input, which is used to adjust the importance of the control input in the cost function.

[0131] E N : Terminal state error vector, used to consider the state error at the end of the prediction time domain in the cost function.

[0132] F: The weight matrix of the terminal state error, which is used to adjust the importance of the terminal state error in the cost function

[0133] The cost function J is minimized by optimizing u(k), u(k+1), u(k+2), ..., u(k+j) (i.e., the control inputs for the next few steps).

[0134] Only the control input u(k) calculated in the first step is applied to the system (rotary kiln system), which is the control action actually applied to the system.

[0135] Then, based on this control input, it enters the next state. In the next control cycle, the MPC algorithm repeats the above steps, using the new current state for prediction and optimization.

[0136] The core of the MPC algorithm is that it not only considers the current control effect, but also optimizes the control strategy by predicting future behavior while dealing with the constraints in the system. This approach makes MPC perform well in dealing with complex systems, especially when future behavior and system constraints need to be considered.

[0137] Specifically, after the final control strategy is implemented, the system monitors and obtains the status information of the rotary kiln in real time. This information may include multiple parameters such as temperature, pressure, flow, flame characteristics, etc., which fully reflect the working conditions of the rotary kiln.

[0138] The current state information is analyzed using the Model Predictive Control (MPC) algorithm. This algorithm combines historical data with a predictive model to dynamically optimize the control strategy by considering future operating conditions and goals. MPC generates the actual control strategy by optimizing the deviation between the current state and the desired goal.

[0139] According to the optimized actual control strategy, the rotary kiln system is adjusted and controlled, including setting operating parameters such as fuel flow, air volume and power. These adjustments are aimed at ensuring that various indicators of the calcination process are maintained within the optimal range.

[0140] Therefore, by acquiring status information in real time and optimizing processing, the operation of the rotary kiln can be controlled more accurately, temperature and pressure fluctuations can be reduced, and the stability of the calcination process can be ensured. Based on real-time analysis of the current status, the system can promptly identify problems and adjust control strategies, quickly respond to changes or anomalies in the production process, and reduce potential production accidents.

[0141] At the same time, precise control of various operating parameters can effectively reduce unnecessary energy consumption, improve the economy of the production line, and thus bring cost savings. And through continuous optimization of the actual control strategy, it can ensure that the quality of titanium dioxide meets the expected standards and avoid product failure or quality fluctuations caused by parameter drift.

[0142] Finally, implementing more precise control measures can help improve production efficiency, shorten calcination time, and increase overall output. The application of model predictive control enables the system to flexibly adjust according to different production needs and environmental changes, promoting the adaptability and flexibility of production.

[0143] In summary, this control process significantly improves the overall performance and economic benefits of the rotary kiln calcining system through real-time monitoring and dynamic optimization, providing a strong guarantee for the high-quality production of titanium dioxide.

[0144] Optionally, the rotary kiln system includes a rotary kiln, the current state information includes temperature information; and obtaining the current state information includes:

[0145] Acquire multiple temperature positions on the axial center line of the kiln chamber of the rotary kiln;

[0146] For one temperature position, weighted processing is performed on multiple temporary temperature points corresponding to the temperature position to obtain corresponding temperature data, and the temperature information is obtained based on the temperature data of all the temperature positions, wherein the temporary temperature point is located in a preset range on a plane that includes the corresponding temperature position and is perpendicular to the axial center line.

[0147] Specifically, a furnace flame observation camera is deployed in the rotary kiln to monitor the temperature points inside the rotary kiln, ensuring that all spaces in the kiln are monitored, thereby achieving non-contact measurement of the temperature during the calcination process. At the same time, the data of each temperature point monitored is analyzed to develop a process control temperature curve for rotary kiln calcination.

[0148] In practical applications, such as Figure 2 As shown in the figure, the kiln chamber schematic diagram of the rotary kiln; multiple temperature calibration points are set on the axial center line of the kiln chamber. Since the measurement accuracy of the furnace flame observation camera is generally around 1%, and the temperature in the rotary kiln is within 1200°C, with such measurement accuracy, the temperature measurement point data needs to be processed before it can be used to establish the rotary kiln calcination system model. Therefore, at a certain temperature position on the axial center line of the kiln chamber, within a preset range on a plane perpendicular to the axial center line, several temperature measurement points (temporary temperature points) are evenly taken, and a weighted average is performed on them. The weighted average value is used as the input value and introduced into the calcination control model.

[0149] In some embodiments, such as Figure 3 As shown in the figure, the control optimization diagram of titanium dioxide rotary kiln; based on the current batch production information of titanium dioxide to be produced and the corresponding preset batch quality information, the advanced controller (the calcination system model of the rotary kiln) is input, and after the prediction model (process dynamic module), the rolling optimization process and the feedback correction process, the actual parameters (control strategy) are obtained, and based on the set parameter (controlled variable) limit (such as Figure 4 As shown, the linear fitting schematic diagram of each controlled variable) regulates the corresponding parameter loop controller (such as the flame value loop controller).

[0150] In some embodiments, such as Figure 5 As shown in the figure, the advanced control path flow chart of the rotary kiln system is used to optimize the production process and quality control of the rotary kiln; specifically, it includes:

[0151] 1. Set value (desired quality target): Enter the set value, which refers to the quality standard you want to achieve, such as flame value, heat load, etc.

[0152] 2. Get the information of the current batch: the status and production information of the current batch of rotary kiln, and collect real-time data through the monitoring and data acquisition system.

[0153] 3. Multivariable controller (rotary kiln calcination system model): analyzes current information and compares it with real-time data based on set values ​​to generate control instructions.

[0154] 4. Dynamically update process models: Update process models based on new real-time data to ensure that control strategies adapt to changing production conditions.

[0155] 5. Update process recipe: Based on the results of process model update, adjust the process recipe to optimize production parameters.

[0156] 6. Real-time monitoring and diagnosis: Real-time monitoring of the operating status of the rotary kiln, diagnosis of problems, and ensuring the stability and safety of the production process.

[0157] 7. Implement control: Apply updated control strategies to production equipment to achieve real-time control of rotary kiln.

[0158] 8. Monitoring of rotary kiln status: Monitor the current status of the rotary kiln and feed back to the control system for timely adjustment.

[0159] 9. Feedback loop: The entire system forms a closed loop, and the control strategy is continuously corrected and optimized through feedback information to ensure that the set quality standards are met.

[0160] This system can effectively solve the problem of temperature parameter and performance index drift during the calcination process through real-time data feedback and multi-variable control, and can correct the deviation in time. At the same time, it realizes the multi-variable parameter decoupling, parameter prediction and dynamic parameter correction of the titanium dioxide rotary kiln calcination system, which can more accurately control the calcination process inside the rotary kiln, ensure that the production operation is carried out according to the established goals, avoid the problem of excess or substandard product quality, reduce production energy consumption, and ensure the economic operation of the production process, that is, improve production efficiency and product quality, and enhance the ability to adapt to uncertainties in the production process.

[0161] like Figure 6 As shown, a control device 600 for calcining titanium dioxide provided in an embodiment of the present disclosure is applied to a rotary kiln system. The control device for calcining titanium dioxide includes:

[0162] An acquisition unit 610 is used to acquire current batch production information of the titanium dioxide to be produced and corresponding preset batch quality information;

[0163] The regulating unit 620 inputs the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy; and controls the rotary kiln system according to the final control strategy.

[0164] like Figure 7As shown, a control system 700 for calcining titanium dioxide provided by an embodiment of the present disclosure includes a memory 710 and a processor 720; the memory 710 is used to store a computer program; the processor 720 is used to implement the control method for calcining titanium dioxide as described above when executing the computer program.

[0165] In other words, a control system 700 for calcining titanium dioxide includes a memory 710 and a processor 720 coupled to the memory 710; the memory 710 is configured to store a computer program; the processor 720 is configured to perform the following operations when executing the computer program:

[0166] Obtaining the current batch production information and the corresponding preset batch quality information of the titanium dioxide to be produced;

[0167] Inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy;

[0168] The rotary kiln system is controlled according to the final control strategy.

[0169] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the control method for calcining titanium dioxide as described above is implemented.

[0170] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0171] Obtaining the current batch production information and the corresponding preset batch quality information of the titanium dioxide to be produced;

[0172] Inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy;

[0173] The rotary kiln system is controlled according to the final control strategy.

[0174] A control system 700 for calcining titanium dioxide, which can be used as a server or client of the present disclosure, will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The control system 700 for calcining titanium dioxide is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The control system 700 for calcining titanium dioxide can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0175] The control system 700 for calcining titanium dioxide includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0176] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme of the present disclosure. In addition, each functional unit in each embodiment of the present disclosure can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0177] Although the disclosure is disclosed as above, the protection scope of the disclosure is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the protection scope of the disclosure.

Claims

1. A method for controlling the calcination of titanium dioxide, characterized in that: A rotary kiln system for producing titanium dioxide, wherein the control method for calcining the titanium dioxide comprises: Obtaining the current batch production information and the corresponding preset batch quality information of the titanium dioxide to be produced; Inputting the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy; The rotary kiln system is controlled according to the final control strategy.

2. The method for controlling the calcination of titanium dioxide according to claim 1, characterized in that: The calcination control model includes a process dynamic module, and the current batch production information and the corresponding preset batch quality information are input into the calcination control model to obtain an optimal control strategy, including: Predicting the current batch production information through the process dynamic module to obtain temporary predicted quality information within a future preset period; The optimal control strategy is obtained according to the temporary predicted quality information and the preset batch quality information.

3. The method for controlling the calcination of titanium dioxide according to claim 2, characterized in that: The obtaining the optimal control strategy according to the temporary predicted quality information and the preset batch quality information includes: Get the preset optimization objective function; Obtaining deviation data according to the temporary predicted quality information and the preset batch quality information; Based on the preset optimization objective function, the optimal control strategy is obtained according to the deviation data.

4. The method for controlling the calcination of titanium dioxide according to claim 2, characterized in that: After controlling the rotary kiln system according to the optimal control strategy, the method further comprises: Obtaining the current actual batch quality information of the titanium dioxide to be produced; Correcting the temporary predicted quality information based on the current actual batch quality information to obtain corrected temporary predicted quality information; The current optimal control strategy is obtained according to the corrected temporary predicted quality information and the preset batch quality information.

5. The method for controlling the calcination of titanium dioxide according to claim 2, characterized in that: The current batch production information includes flame value, hot air value, feed amount and kiln temperature, and the temporary predicted quality information includes predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced; the current batch production information is predicted by the process dynamic module to obtain temporary predicted quality information within a preset time period in the future, including: The flame value, the hot air value, the feed amount and the temperature in the kiln are comprehensively processed through the process dynamic module to obtain the predicted whiteness, predicted gloss and predicted hiding power of the titanium dioxide to be produced in a future preset time period.

6. The method for controlling the calcination of titanium dioxide according to claim 1, characterized in that: The control method for calcining titanium dioxide also includes: After controlling the rotary kiln system according to the final control strategy, obtaining current state information of the rotary kiln system; Based on the model predictive control algorithm, optimization processing is performed according to the current state information to obtain an actual control strategy, and the rotary kiln system is controlled according to the actual control strategy.

7. The method for controlling the calcination of titanium dioxide according to claim 6, characterized in that: The model predictive control algorithm includes: Among them, J is the cost function, is the transpose of the state error, which represents the difference between the current state information of the system and the corresponding predicted state at time step k. is the transpose of the control input, which represents the actual control strategy applied to the system at time step k. Q and R are weight matrices, respectively. is the transpose of the terminal state error, F is the terminal cost matrix, and m is the length of the prediction time domain.

8. The method for controlling the calcination of titanium dioxide according to claim 1, characterized in that: The rotary kiln system includes a rotary kiln, the current state information includes temperature information; and obtaining the current state information includes: Acquire multiple temperature positions on the axial center line of the kiln chamber of the rotary kiln; For one temperature position, weighted processing is performed on multiple temporary temperature points corresponding to the temperature position to obtain corresponding temperature data, and the temperature information is obtained based on the temperature data of all the temperature positions, wherein the temporary temperature point is located in a preset range on a plane that includes the corresponding temperature position and is perpendicular to the axial center line.

9. A control device for calcining titanium dioxide, characterized in that: A rotary kiln system for producing titanium dioxide, wherein the control device for calcining the titanium dioxide comprises: An acquisition unit, used to acquire current batch production information of the titanium dioxide to be produced and corresponding preset batch quality information; The regulating unit inputs the current batch production information and the corresponding preset batch quality information into the calcination control model to obtain a final control strategy; and controls the rotary kiln system according to the final control strategy.

10. A control system for calcining titanium dioxide, characterized in that: It comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement the control method for calcining titanium dioxide as claimed in any one of claims 1 to 8 when executing the computer program.

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