Silver nitrate crystal impurity content real-time prediction and process parameter multi-objective optimization system

Abstract

The invention relates to the technical field of intelligent control and optimization in the chemical production process, in particular to a silver nitrate crystal impurity content real-time prediction and process parameter multi-objective optimization system, which comprises a data acquisition layer, an analysis prediction layer, a multi-objective optimization layer and a dynamic control layer, the data acquisition layer obtains multi-source heterogeneous data; the analysis and prediction layer predicts impurity content and correlates process parameters by means of deep learning and a crystallization phase change kinetic model; the multi-objective optimization layer aims at minimizing impurities, maximizing crystal yield and minimizing energy consumption, and optimal parameters are solved by using an improved algorithm; the dynamic control layer carries out real-time regulation and control according to an optimization result, and abnormal processing and closed-loop feedback are set; the system integrates a process knowledge graph and supports case-based reasoning to realize rapid parameter recommendation. Accurate monitoring, intelligent optimization and dynamic control of the silver nitrate crystallization process are achieved, the impurity content is effectively reduced, the crystal yield and the energy consumption efficiency are cooperatively improved, and the system robustness is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control and optimization of chemical production processes, in particular to a system for real-time prediction of impurity content in silver nitrate crystallization and multi-objective optimization of process parameters. BACKGROUND

[0002] In modern industrial production, silver nitrate is widely used in high-end fields such as electronics, pharmaceuticals, and electroplating as an important chemical raw material. These fields have strict requirements on the purity and quality of silver nitrate products. However, the current silver nitrate crystallization process still faces many technical bottlenecks.

[0003] From the perspective of impurity control, traditional processes rely on manual experience or simple offline detection methods, making it difficult to accurately monitor the dynamic changes of impurity elements (such as iron, copper, lead, manganese, etc.) in the crystallization process in real time. The mixing of impurities not only affects the purity of silver nitrate products, but also may lead to a decrease in crystal quality, which cannot meet the needs of high-end applications. Moreover, there is a complex nonlinear relationship between impurity content and various process parameters (such as crystallization temperature, stirring rate, etc.), making it difficult for traditional methods to effectively establish accurate correlation models, thus unable to effectively predict and control impurity content.

[0004] In terms of process parameter optimization, existing silver nitrate crystallization processes usually optimize a single target (such as crystal yield or energy consumption), lacking the ability to coordinate the optimization of multiple targets such as impurity content, crystal yield, and energy consumption. Since these targets often have a mutually restrictive relationship, optimization of a single target may lead to deterioration of other targets, making it difficult to maximize production efficiency. At the same time, traditional optimization algorithms are prone to local optimal solutions when dealing with complex constraint conditions and large-scale search spaces, making it difficult to find globally optimal process parameter combinations.

[0005] From the perspective of system control, most silver nitrate crystallization systems use open-loop control or simple closed-loop control methods, lacking the ability to quickly respond and adaptively adjust to abnormal conditions (such as sudden exceedance of impurity content, equipment failure, etc.). Once an abnormality occurs, it is difficult to take effective measures in a timely manner, which may lead to production interruptions, product quality degradation, and even safety hazards. In addition, traditional systems also have difficulty in effectively accumulating and reusing process knowledge, and each time a new production condition or product demand is encountered, process debugging needs to be performed again, consuming a large amount of time and resources.

[0006] Therefore, a system for real-time prediction of impurity content in silver nitrate crystallization and multi-objective optimization of process parameters is proposed to address the above problems. SUMMARY

[0007] The present application aims to provide a silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system, comprising: A data acquisition layer: integrating an online sensor network, real-time acquisition of multi-source heterogeneous data in the silver nitrate crystallization process, including crystallizer temperature, stirring rate, solution density, mother liquor ion concentration, raw material silver purity, and real-time impurity element content data detected by inductively coupled plasma atomic emission spectrometry / mass spectrometry; An analysis and prediction layer: An impurity content dynamic prediction model constructed based on deep learning, with real-time data from the data acquisition layer as input, and the content prediction values and confidence intervals of iron, copper, lead, and manganese impurity elements as output; Fusion of crystallization phase transition kinetics model, correlation of process parameters with crystal morphology, particle size distribution, and impurity inclusion effect; A multi-objective optimization layer: Minimizing impurity content, maximizing crystal yield, and minimizing energy consumption as optimization objectives, multi-objective functions are established; Improved non-dominated sorting genetic algorithm with constraints is used to generate a Pareto optimal solution set; A dynamic control layer: generating process parameter adjustment instructions according to the optimization solution set, real-time control of crystallizer temperature, stirring rate, cooling curve, and additive dosage.

[0009] As a preferred scheme, the impurity content dynamic prediction model comprises: A time series feature extraction module: using an improved adaptive gated recurrent unit to process time series data of temperature, stirring rate, and density; its hidden state update process is: Update gate and reset gate are calculated through input vector and previous moment hidden state; Generate candidate hidden state according to reset gate and input vector; Combine update gate, candidate hidden state, and previous hidden state to generate current hidden state; Introduce process feature mask matrix to strengthen the influence of key parameters, and calculate adaptive weight distribution through scaling dot product attention mechanism; A composition correlation module: modeling the interaction of iron, copper, lead, and manganese impurity elements through a graph convolutional neural network; the node feature update process is: Add self-connection to the adjacency matrix and perform normalization processing; Linearly transform the normalized adjacency matrix and the current node feature matrix, then superimpose the metal ion bond energy correlation matrix; output the updated node features through a nonlinear activation function; a dynamic correction module: updating model weights based on real-time detection data.

[0010] As a preferred solution, the multi-objective function is defined as the minimum weighted sum of the total impurity content, the reciprocal of the crystal yield, and the energy consumption; the weight coefficients of the multi-objective function are dynamically adjusted according to the purity of the raw material silver; the constraint conditions include a crystallization temperature range of 110-130℃ and a stirring rate range of 60-80rpm.

[0011] As a preferred solution, the improved non-dominated sorting genetic algorithm includes: an adaptive chaotic mutation operator: a first chaotic control parameter is used to dynamically calculate the mutation probability in the early stage of algorithm iteration; a second chaotic control parameter is used to dynamically calculate the mutation probability in the later stage of iteration; the iteration threshold is set to 40% of the total number of iterations; a process constraint processing module: a double penalty function is used to handle the constraint violation problem; a square penalty term is calculated for inequality constraints, and an absolute value penalty term is calculated for equality constraints; the penalty coefficient is adjusted in conjunction with the population diversity index; a solution set evaluation module: the entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS) are used to screen the optimal parameter combination.

[0012] As a preferred solution, the dynamic control layer further includes: an abnormal condition handling unit: when the predicted impurity content exceeds 1ppm, the dosing instructions of silver oxide / aluminum nitrate compound or nitric acid modified activated carbon are automatically triggered; a closed-loop feedback unit: based on the kettle bottom temperature, the concentration endpoint judgment logic is real-time corrected, and the temperature error is controlled to be ≤±2℃.

[0013] As a preferred solution, the data acquisition layer detects 21 impurity elements in silver by thiourea complexation-inductively coupled plasma mass spectrometry (ICP-MS); the detected elements include copper, bismuth, iron, lead, gold, and platinum, and the detection limit is ≤0.00001%.

[0014] As a preferred solution, the update strategy of the dynamic correction module is: after the completion of each batch of crystallization, the predicted value is compared with the actual detection data; if the prediction deviation of three consecutive batches exceeds 5%, the transfer learning mechanism is started to fine-tune the model full connection layer parameters.

[0015] As a preferred solution, the system output end integrates the process knowledge graph, stores the historical optimization solution set, impurity inhibition strategy and energy consumption mapping relationship, and supports the fast recommendation of process parameters based on case-based reasoning.

[0016] As a preferred solution, the crystallization phase change kinetics model comprises: The rate of change of crystallization conversion rate is proportional to the temperature-dependent rate constant; The logarithmic conversion term and the ratio of the unconverted phase are exponentially related; The temperature gradient norm is introduced as a quantitative representation of the stirring rate, and the impurity encapsulation effect is adjusted by the influence factor.

[0017] As a preferred solution, the penalty coefficient adjustment rule is: The inequality constraint penalty coefficient is linearly enhanced with the population diversity index; The equality constraint penalty coefficient is exponentially attenuated with the population diversity index; When the population diversity decreases, the process hard constraints are preferentially guaranteed.

[0018] As can be seen from the above technical solutions provided by the present application, the silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system provided by the present application has the following beneficial effects: Product quality guarantee: the impurity content dynamic prediction model based on deep learning can accurately capture the complex correlation between process parameters and impurity generation, dynamically adjust the prediction strategy combined with real-time monitoring data, realize early prediction and accurate control of impurity content; combined with the intelligent impurity removal strategy of the dynamic control layer, the product impurity level can be effectively reduced to meet the strict requirements of high-end applications for silver nitrate purity, significantly improve the product quality stability and consistency; Production efficiency improvement: the multi-objective optimization layer takes impurity control, crystal yield and energy consumption as the core optimization targets, and searches for the optimal process parameter combination through an improved intelligent algorithm to realize multi-index collaborative optimization; the dynamic control layer accurately lands the optimization results to ensure that the process parameters match the production demand in real time, effectively improve the crystal growth efficiency, reduce energy and raw material waste, and comprehensively improve the production efficiency and economic benefit; System stability and reliability: the dynamic control layer has strong abnormal condition handling capability, and can quickly identify and respond to impurity exceeding, equipment failure and other sudden conditions through real-time monitoring of key indicators; with the help of fault isolation, redundancy switching and emergency handling mechanism, the impact of abnormal conditions on production is minimized, the system is continuously and stably operated, the downtime maintenance time is reduced, and the production reliability is improved; Intelligent decision support: The process knowledge graph integrates historical optimization experience and real-time production data, and builds a complete process knowledge network; based on case-based reasoning technology, the system can quickly recommend effective process parameters for different production conditions, providing scientific decision-making basis for operators; at the same time, the continuous updating and learning ability of the knowledge graph promotes the system from experience-driven to data-driven upgrade, accelerating the process optimization and innovation process; Knowledge inheritance and innovation: The process knowledge graph stores expert experience and historical cases in a structured manner, breaking the limitations of knowledge inheritance and effectively avoiding the loss of experience due to personnel changes; enterprises can mine potential optimization directions based on the knowledge graph, explore new processes and strategies, and promote the continuous progress of silver nitrate crystallization technology, enhancing the core competitiveness of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The present application is a kind of silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system overall structure schematic diagram. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present application clearer, the following will be further described in detail in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and not to limit the present application.

[0021] In order to better understand the above technical scheme, the above technical scheme will be described in detail in combination with the drawings and specific embodiments of the present application.

[0022] As shown in the drawings, Figure 1 The present application provides a kind of silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system, comprising: Data acquisition layer: integrate online sensor network, real-time acquisition in the process of silver nitrate crystallization multi-source heterogeneous data, including crystallization kettle temperature, stirring rate, solution density, mother liquor ion concentration, raw material silver purity, and the real-time impurity element content data detected by inductively coupled plasma atomic emission spectrometry / mass spectrometry; Analysis and prediction layer: Impurity content dynamic prediction model based on deep learning, input is the real-time data of data acquisition layer, output is the content prediction value and confidence interval of iron, copper, lead and manganese impurity elements; Fusion crystallization phase transition kinetics model, related to process parameters and crystal morphology, particle size distribution and impurity inclusion effect; Multi-objective optimization layer: With the minimum impurity content, the maximum crystal yield and the minimum energy consumption as optimization objectives, a multi-objective function is established; Improved non-dominated sorting genetic algorithm with constraints is used to generate the Pareto optimal solution set; Dynamic control layer: generate process parameter adjustment instructions according to the optimal solution set, real-time control the temperature of the crystallization kettle, stirring rate, cooling curve and additive dosage.

[0023] In this embodiment, the data acquisition layer is the basis of the real-time prediction of the impurity content of silver nitrate crystals and the multi-objective optimization system of process parameters, and undertakes the task of collecting key data, providing the basis for subsequent analysis and prediction. The following aspects are described in detail: function overview, component units, technical principles, work flow and application value, etc. I. Overall function overview: The data acquisition layer collects multi-source heterogeneous data in the silver nitrate crystallization process in real time and accurately through the integration of online sensor networks, covering physical parameters, chemical parameters, and impurity detection data, etc. These data are not only the input basis for the dynamic prediction model of impurity content, but also the key basis for multi-objective optimization and dynamic control of process parameters. The data acquisition layer also needs to preliminarily process the collected data to ensure the accuracy, integrity and effectiveness of the data, providing reliable support for the stable operation and accurate decision-making of the system. II. Submodule composition and function: (1) Physical parameter acquisition unit: Temperature acquisition: Install high-precision temperature sensors (such as platinum resistance temperature sensors) at different positions of the crystallization kettle (such as the kettle wall, the center of the solution, and the vicinity of the stirring paddle) to collect temperature data in real time during the crystallization process. These sensors have fast response and high precision characteristics, can capture small changes in temperature, and have a sampling frequency of up to 10 times per second and a measurement accuracy of ±0.1°C, ensuring the real-time distribution of the temperature field inside the crystallization kettle. Stirring rate acquisition: Connect a photoelectric encoder or a magneto-electric speed sensor to the stirring shaft to monitor the rotational speed of the stirrer in real time. The sensor converts the mechanical rotation signal into an electrical signal, which is processed by a signal conditioning circuit to output a pulse signal or an analog signal proportional to the stirring rate, achieving accurate measurement of the stirring rate with an error of not more than ±1 revolution / minute. Solution density acquisition: Use a vibrating densimeter or a differential pressure densimeter to measure the density of the solution by measuring the vibration frequency or pressure difference. These densimeters can adapt to changes in the composition and concentration of the solution during the crystallization process, providing accurate density data with a measurement range of 1.0-3.0 g / cm 3 and an accuracy of ±0.005 g / cm 3 ; (2) Chemical parameter acquisition unit: Mother liquor ion concentration collection: Use ion selective electrode (ISE) or ion chromatograph to monitor the concentration of key ions (such as nitrate ions, silver ions, etc.) in the mother liquor in real time; ion selective electrode produces a potential signal related to ion concentration by selective reaction with specific ions in the solution, which is amplified and converted to ion concentration data; ion chromatograph separates and detects different ions to accurately analyze the concentration of multiple ions, with a detection limit of ppb level; Raw material silver purity analysis: Use thiourea complexation-inductively coupled plasma mass spectrometry (ICP-MS) to detect impurity elements in raw material silver; first separate impurity elements in silver by thiourea complexation, then use ICP-MS to quantitatively analyze 21 impurity elements (including copper, bismuth, iron, lead, gold, platinum, etc.), with a detection limit of ≤0.00001%, providing accurate purity information of raw material silver for the system; (Three) Impurity detection unit: Real-time impurity element content detection: Use inductively coupled plasma atomic emission spectrometry (ICP-AES) or inductively coupled plasma mass spectrometry (ICP-MS) to detect real-time impurity elements (such as iron, copper, lead, manganese, etc.) during crystallization; ICP-AES excites atoms in the sample to emit light of a specific wavelength, and determines the element content according to the intensity of the light; ICP-MS ionizes the sample and separates and detects ions according to their mass-to-charge ratio, with higher sensitivity and resolution, enabling trace analysis of impurity element content with a detection accuracy of ±0.1 ppm; Data preprocessing and transmission: Preprocess the detected impurity element content data by filtering, calibration, etc. to remove noise and outliers; then transmit the processed data in real time to the analysis and prediction layer through a data transmission module (such as Ethernet, industrial bus, etc.) to ensure the timeliness and accuracy of the data; (Four) Data transmission and storage unit: Data transmission: Use industrial communication protocols (such as Modbus, Profinet, etc.) and network architecture to achieve reliable transmission of sensor data; establish redundant communication links to automatically switch to backup lines when the main communication line fails, ensuring continuous data transmission; at the same time, encrypt the transmitted data to prevent data leakage and tampering, ensuring data security; Data storage: Set up a local data storage server to store the collected raw data and preprocessed data in real time; use distributed storage technology to improve data storage reliability and scalability; data storage time can be set according to requirements, generally saving at least one year of historical data for subsequent data analysis, fault tracing and process optimization; Three, key technology principles: (1) Sensor technology principles: Various sensors achieve parameter measurement based on different physical and chemical principles; for example, platinum resistance temperature sensors utilize the characteristic that the resistance value of metal platinum changes with temperature to determine temperature by measuring resistance value; ion-selective electrodes achieve measurement of specific ion concentration based on ion exchange and membrane potential principles; the high precision, high reliability, and fast response characteristics of these sensors are key to ensuring accurate data collection; (2) Data communication and transmission principles: Industrial communication protocols are used to achieve data interaction between sensors and the system; these protocols define data format, transmission method, and communication rules to ensure accurate and efficient data transmission; redundant communication links automatically switch when the main link fails, ensuring uninterrupted data transmission; data encryption techniques use symmetric encryption or asymmetric encryption algorithms to encrypt transmitted data, ensuring that only authorized devices can decrypt and read the data, ensuring data security; (3) Data preprocessing principles: Data preprocessing mainly includes filtering, calibration, and outlier processing; filtering uses digital filtering algorithms (such as mean filtering, median filtering, Kalman filtering, etc.) to remove noise and interference in data; calibration corrects sensor measurement results by comparing with standard equipment or known data to improve data accuracy; outlier processing identifies and removes abnormal data points through statistical analysis methods (such as the 3σ principle, box plot method, etc.) to ensure input data reliability; Four, module workflow: (1) Initialization phase: After the data acquisition layer starts, it performs self-checking on all sensors and communication devices to check the connection status, power supply, and working status of the communication module to ensure normal operation of hardware devices; Load calibration parameters of sensors, communication protocol configuration information, etc., establish communication connection with the analysis and prediction layer, and prepare to receive data acquisition instructions and send collected data; (2) Data acquisition phase: According to the set sampling frequency, each sensor collects physical parameters, chemical parameters, and impurity detection data during the silver nitrate crystallization process in real time; different types of sensors use corresponding acquisition methods and frequencies according to their characteristics and measurement requirements, such as temperature sensors collecting 10 times per second and raw silver purity analysis detecting once per batch; Perform preliminary processing on the collected raw data, such as converting analog signals to digital signals and encoding data, to facilitate subsequent transmission and processing; (3) Data preprocessing phase: The collected data is filtered to remove noise and interference, improving data quality; The data is calibrated according to the calibration parameters of the sensor to correct measurement errors and ensure data accuracy; Anomaly detection algorithm is used to identify and eliminate abnormal data points to ensure the reliability of input data; (4) Data transmission and storage phase: The preprocessed data is transmitted to the analysis and prediction layer in real time through the data transmission module according to the set communication protocol and network architecture; At the same time, the original data and preprocessed data collected are stored in the local data storage server, and a data index and backup mechanism are established to facilitate subsequent data query and analysis; (5) End phase: When the system stops running or receives a stop instruction, the data acquisition layer stops data acquisition, processing and transmission operations, closes communication connections with other modules, saves running records and configuration parameters, and releases system resources; Five, the application value of the module: (1) Ensure accurate system operation: Through high-precision and real-time data acquisition, accurate data basis is provided for impurity content dynamic prediction model and multi-objective optimization, ensuring that the system can accurately predict impurity content, optimize process parameters, and realize fine control of silver nitrate crystallization process, improving product quality and production efficiency; (2) Improve fault diagnosis capability: Rich multi-source heterogeneous data helps to discover abnormal conditions in the crystallization process in a timely manner, and combined with data analysis and fault diagnosis algorithms, it can quickly locate the fault cause and position, providing strong support for fault handling, reducing downtime and production loss; (3) Support process optimization and improvement: The storage and analysis of historical data provide data basis for process optimization; by comparing and mining data under different process conditions, potential optimization space can be found to improve production process, improve silver nitrate crystallization quality and yield, and reduce production cost; (4) Realize production process transparency: Real-time and accurate data acquisition enables production managers to fully understand the running state of the silver nitrate crystallization process, realizes visual and transparent management of the production process, facilitates the development of scientific and reasonable production plans and decisions, and improves production management level.

[0024] In this embodiment, the impurity content dynamic prediction model includes: The time-series feature extraction module uses an improved adaptive gated loop unit to process time-series data on temperature, stirring rate, and density; its hidden state update process is as follows: The update gate and reset gate are calculated using the input vector and the hidden state from the previous moment. Generate candidate hidden states based on the reset gate and the input vector; The current hidden state is generated by combining the update gate, candidate hidden states, and the previous hidden state. A process feature mask matrix is ​​introduced to enhance the influence of key parameters, and an adaptive weight distribution is calculated through a scaled dot product attention mechanism. Composition association module: Models the interactions of iron, copper, lead, and manganese impurity elements using a graph convolutional neural network; the node feature update process is as follows: Add self-joins to the adjacency matrix and normalize it; The normalized adjacency matrix and the current node feature matrix are linearly transformed and then superimposed with the metal ion bond energy correlation matrix; The updated node features are output using a nonlinear activation function; Dynamic correction module: updates model weights based on real-time detection data; Furthermore, the analysis and prediction layer, as the core component of the real-time prediction system for impurity content in silver nitrate crystallization and the multi-objective optimization system for process parameters, achieves accurate analysis and prediction of the crystallization process through deep integration of data and models. The following section will elaborate on this layer in detail, including its functional architecture, core modules, and technical principles, using specific formulas: I. Overall Function Overview: The analysis and prediction layer is based on the multi-source heterogeneous data acquired by the data acquisition layer, and constructs two core model systems: a dynamic prediction model for impurity content and a crystallization phase transformation kinetic model. The former, based on deep learning algorithms, uses complex mathematical operations and parameter iterations to predict the content of impurity elements such as iron, copper, lead, and manganese in real time, and outputs the predicted values ​​and confidence intervals. The latter, based on the physicochemical laws of the crystallization process, establishes a quantitative model that correlates process parameters with crystal morphology, particle size distribution, and impurity encapsulation effects. The two models work together to provide key decision parameters for the multi-objective optimization layer, thereby achieving intelligent control of the silver nitrate crystallization process. II. Submodule Composition and Functions: (a) Dynamic prediction model for impurity content: Temporal feature extraction module An improved adaptive gated loop unit (GRU) is used to process time-series data such as temperature, stirring rate, and density. Its state update formula is as follows: (in, for The time-input vector includes process parameters such as temperature, stirring rate, and density; for Always hide your status; This is an update gate used to control the proportion of hidden state information from the previous time step that is passed to the current time step; To reset the door, determine the degree to which the hidden state information from the previous moment is forgotten; This is the candidate hidden state; Adaptive attention weights for enhancing process features are used to highlight key process features; The sigmoid activation function compresses the data to the range of 0-1. It is the hyperbolic tangent activation function; , , , The weight matrix is ​​trainable. , , , It is a trainable bias vector; (This is a process feature mask matrix, containing temperature gradient and stirring rate variation features). Through this set of formulas, the model can effectively capture the dynamic variation features in time series data and enhance key information by combining process characteristics. Ingredient association module: The interaction between iron, copper, lead, and manganese impurities is modeled using a graph convolutional neural network (GCN). The node update formula is as follows: (in, To add self-joins to the adjacency matrix, the nodes are iron, copper, lead, and manganese elements, describing the relationships between the elements; This is the node degree matrix, used to normalize the adjacency matrix; The feature matrix of the nodes in the l-th layer; Let l be the trainable weight matrix of the l-th layer; This is the correlation matrix of metal-ion bond energies, reflecting the elemental bonding strength; Factors affecting the crystallization process; (This is a linear rectified function used to introduce nonlinear relationships). This formula uses matrix operations to aggregate and update information between nodes, thereby uncovering the complex interactions between impurity elements. Dynamic correction module: The model weights are updated based on real-time inductively coupled plasma detection data, with a prediction error ≤ ±0.5ppm; the model update strategy uses the following formula: (in, These are the model parameters at time t+1; for Model parameters at time points; The learning rate controls the step size for parameter updates; is a loss function about the parameters of the gradient); after each batch of crystallization is completed, the actual impurity detection data is compared with the predicted value, if the prediction deviation is > 5% for 3 batches in a row, the transfer learning mechanism is started, and the pre-training model is used to fine-tune the parameters of the fully connected layer based on small sample data; (II) Crystallization phase change kinetics model: The crystallization phase change kinetics model is an improved four-tuple equation: (wherein, is the crystallization conversion rate; is the crystallization rate; is the temperature-dependent crystallization rate constant, reflecting the influence of temperature on the crystallization rate; is the Avrami index, describing the dimension and mechanism of crystal growth; is the temperature gradient influence factor; is the temperature gradient norm in the crystallization kettle, measuring the degree of unevenness of the temperature distribution); the model correlates the stirring rate and the temperature gradient distribution, quantifies the impurity wrapping effect, and provides a theoretical basis for process optimization; (III) Model cooperative interaction unit: In the model cooperative process, parameter optimization is achieved through data fusion and error feedback adjustment, and the error calculation formula is: (wherein, is the root mean square error; is the number of samples; is the actual measured value; is the model predicted value); according to the error value, the parameters of the impurity content prediction model and the crystallization phase change kinetics model are dynamically adjusted, forming a closed-loop optimization mechanism; III. Key technical principles: (I) Deep learning prediction technology principle: The deep learning model updates parameters through the backpropagation algorithm, and the backpropagation formula is: (wherein, is a loss function about the weights of the gradient; is the gradient of the loss function with respect to the model output; is the gradient of the model output with respect to the weights ); by calculating the gradient and updating the weights in the opposite direction of the gradient, the prediction error is continuously reduced, and the model accuracy is improved; (II) Crystallization phase change kinetics principle: The crystallization phase change kinetics is based on the classical Avrami equation, by introducing a temperature gradient influence factor and temperature gradient norm The influence of stirring rate on temperature distribution is taken into account in the model; the change of stirring rate will change the temperature gradient in the crystallization kettle, and then affect the growth rate of crystals and the inclusion of impurities. Through the model, the relationship between process parameters and crystal quality can be quantitatively analyzed; (Three) Model collaborative optimization principle: Model collaborative optimization is based on data-driven and feedback control principles; the prediction results of impurity content are fused with the output of the crystallization phase change kinetics model to form a comprehensive data set; the accuracy of model prediction is judged by calculating the error index (such as root mean square error ) of the fusion data; if the error exceeds the threshold, the model parameters are adjusted through the back propagation algorithm to realize the collaborative optimization of the two models, and provide more accurate data support for multi-objective optimization; Four, the working process of the module: (One) initialization stage: Load the pre-training parameters of the impurity content dynamic prediction model, including the weight matrix of GRU and GCN 、 、 、 、 , bias vector 、 、 、 , and the initial parameters of the crystallization phase change kinetics model, such as 、 、 ; Establish a real-time data transmission channel with the data acquisition layer and configure data receiving and preprocessing rules; (Two) data receiving and preprocessing stage: Real-time receive temperature, stirring rate, solution density, mother liquor ion concentration, raw silver purity and impurity detection data transmitted by the data acquisition layer; Normalize the time series data, encode the graph structure data, and convert the raw silver purity data into a format that the model can recognize; (Three) model prediction and analysis stage: Input the preprocessed data into the impurity content dynamic prediction model, calculate the hidden state and attention weight through the GRU formula of the time series feature extraction module, and then calculate the interaction of impurity elements through the GCN formula of the composition association module. Finally, output the impurity content prediction value and confidence interval; Input the process parameter data into the crystallization phase change kinetics model, calculate the crystallization conversion rate, crystallization rate and other parameters through the four-tuple equation, and analyze the inclusion effect of impurities; (Four) result output and feedback stage: transmit the impurity content prediction result and the crystallization kinetics analysis result to the multi-objective optimization layer; receive the optimization result and error information fed back by the multi-objective optimization layer, calculate the prediction error according to the error calculation formula, and if the error is out of standard, adjust the model parameters through the parameter update formula of the dynamic correction module to realize the iterative optimization of the model; (five) end phase: When the system stops running or receives a stop instruction, save the latest parameters obtained by model training, close the data transmission channel, and release system resources; Five, application value of the module: (1) realize accurate control of impurities: The impurity content dynamic prediction model controls the prediction error within ±0.5ppm through complex formula operation and parameter optimization, so that the production process can predict the change of impurity content in advance, adjust the process parameters or add impurity removal agent in time, and ensure that the impurity content of silver nitrate product meets the high-end application standard; (2) improve process optimization efficiency: The crystallization phase change kinetics model reveals the internal relationship between process parameters and crystal quality through a quantitative formula, and combined with the multi-objective optimization layer, the crystal yield can be increased by 5%-8%, and the unit energy consumption can be reduced by 10%-15%, significantly improving production efficiency and economic benefits; (3) enhance the self-adaptive ability of the system: Based on the error formula and parameter update formula, combined with the transfer learning mechanism, the dynamic correction module enables the model to quickly adapt to changes in working conditions such as raw material fluctuations and equipment differences, ensuring long-term stable operation of the system; (4) promote the sedimentation of process knowledge: The large amount of data and optimization results generated during model operation can be used to build a process knowledge graph, providing data support for process parameter recommendation and fault diagnosis in new working conditions, and accelerating process optimization iteration.

[0025] In this embodiment, the multi-objective function is defined as the weighted sum minimum of total impurity content, crystal yield reciprocal and energy consumption; the weight coefficient of the multi-objective function is dynamically adjusted according to the raw material silver purity; the constraint conditions include the crystallization temperature range of 110℃ to 130℃ and the stirring speed range of 60 revolutions / minute to 80 revolutions / minute; The improved non-dominated sorting genetic algorithm includes: Adaptive chaotic mutation operator: In the early stage of algorithm iteration, the first chaotic control parameter is used to dynamically calculate the mutation probability; In the later stage of iteration, the second chaotic control parameter is used to dynamically calculate the mutation probability; The iteration threshold is set to 40% of the total number of iterations; Process constraint processing module: A double penalty function is used to handle constraint violation problems; Calculate the square penalty term for inequality constraints and the absolute value penalty term for equality constraints; The penalty coefficient is adjusted in conjunction with the population diversity index; Solution set evaluation module: The optimal parameter combination is selected using the entropy weight method and the approximation ideal solution sorting method; Furthermore, the multi-objective optimization layer, as the decision-making center of the real-time prediction of impurity content and multi-objective optimization system for process parameters in silver nitrate crystallization, undertakes the core task of balancing impurity content, crystal yield, and energy consumption. It transforms the output of the analysis and prediction layer into an executable process parameter optimization scheme through the construction of mathematical models and intelligent algorithms. The following provides a comprehensive and in-depth explanation of the multi-objective optimization layer from the aspects of functional positioning, core modules, technical principles, workflow, and application value: I. Overall Function Overview: The multi-objective optimization layer is based on the predicted impurity content and crystallization kinetic parameters output by the analysis and prediction layer. It constructs a multi-objective function with the core objectives of minimizing impurity content, maximizing crystal yield, and minimizing energy consumption. By introducing a constrained improved non-dominated sorting genetic algorithm (NSGA-II), it solves for the Pareto optimal solution set that satisfies the process constraints. The optimal combination of process parameters is then selected using the entropy-weighted approximation ideal solution sorting method (TOPSIS). Finally, the optimization results are transmitted to the dynamic control layer to achieve real-time control of parameters such as crystallizer temperature and stirring rate, improving production efficiency and economy while ensuring product quality. II. Submodule Composition and Functions: (a) Multi-objective function construction unit: Objective function definition: The multi-objective function is expressed in a weighted summation form, as shown in the formula: (in, Total impurities, expressed in ppm, reflecting product quality; Crystal yield, expressed as a percentage, measures production efficiency; Energy consumption per unit of output, expressed in kilowatt-hours per kilogram, reflects energy consumption. The weights are dynamically adjusted based on the purity of the raw silver; the higher the purity, the more the weights tend to reduce the impurity content. , , This is a multi-objective balance coefficient used to adjust the relative importance of each objective; To optimize the number of scenarios, adjustments can be made based on changes in production batches or operating conditions. Process constraint settings: Introduce hard constraints to limit the feasible region of process parameters: Crystallization temperature: , ensure the crystal growth in the thermodynamic stable region; stirring rate: rpm , balance the mass transfer efficiency and energy consumption; crystallization end temperature: , cooling rate: , ensure the crystal size and purity; (2) improved non-dominated sorting genetic algorithm unit: adaptive chaotic mutation operator: Mutation probability is dynamically generated by piecewise chaotic mapping, the formula is: (where, is the mutation probability of the generation; , is the chaotic control parameter, used to enhance the global search ability in the early stage, improve the local optimization accuracy in the later stage; is the iteration threshold, set to 40% of the total iteration number, used to distinguish the search stage of the algorithm); this mechanism avoids the algorithm falling into local optimum through the ergodicity and randomness of chaotic system; process constraint processing module: The constraint condition is embedded into the fitness calculation by using double penalty function, the formula is: (where, is the objective function value; , is the adaptive penalty coefficient associated with population diversity, dynamically adjusting the cost of constraint violation; is the inequality constraint (such as crystallization temperature, cooling rate limit); is the equality constraint; , are the number of inequality and equality constraints respectively); the adaptive penalty coefficient adjustment formula is: , (where, , is the initial penalty coefficient; is the population diversity index, which is quantified by calculating the Euclidean distance or Hamming distance between individuals, when the diversity decreases, the penalty strength is increased to maintain the feasibility of the solution); solution set generation and screening: Non-dominated sorting: sort the population individuals according to the Pareto dominance relationship, and divide the solutions into different levels of non-dominated frontiers; crowding distance calculation: by calculating the crowding distance of individuals in the same frontier, the solutions with uniform distribution are retained, and the solution set is avoided; Elitist strategy: pass the current optimal solution to the next generation, accelerate the convergence of the algorithm; (Three) optimal solution evaluation and decision unit: Entropy weight-TOPSIS evaluation: Combined with entropy weight method to determine the index weight, TOPSIS method to calculate the closeness degree: Entropy weight calculation: , (Where, is the weight of the th index; is the entropy value of the th index; is the number of solutions, is the number of indexes; is the normalized value of the th solution on the th index); TOPSIS ranking: calculate the Euclidean distance of each solution and the positive and negative ideal solutions, the formula is: , , (Where, is the closeness degree of the th solution; , are the distances to the positive and negative ideal solutions respectively; , are the values of the positive and negative ideal solutions on the th index respectively; is the value of the th solution on the th index); The solution with higher closeness degree is closer to the optimal solution; Decision output: the screened optimal process parameter combination (temperature, stirring rate, cooling curve, etc.) is transmitted to the dynamic control layer and stored in the process knowledge graph for historical case comparison and rapid parameter recommendation; Three, key technology principles: (One) multi-objective optimization theory: Weighted summation method is used to convert multi-objective problem into single objective optimization, and the weight coefficient is adjusted to balance the conflict between different objectives; Process constraints are converted into additional items of objective function through penalty function, to ensure the feasibility of the optimal solution; (Two) improved mechanism of genetic algorithm: Adaptive chaotic mutation: use the ergodicity and randomness of chaotic mapping to expand the search range in the early stage of the algorithm and focus on local optimization in the later stage, to improve the convergence speed and solution quality of the algorithm; Dual penalty function: dynamically balance the constraint violation cost and objective function optimization by adaptive adjustment of the penalty coefficient, avoid generating infeasible solutions; (Three) solution evaluation method: Entropy weight method determines the weight based on the discrete degree of data itself, avoiding subjective factors interference; TOPSIS method selects the optimal scheme with the best comprehensive performance from the Pareto frontier by calculating the distance from the ideal solution, ensuring the scientificity of decision-making; Four, the workflow of the module: (One) initialization stage: Receive the impurity content, crystallization conversion rate and other data output by the analysis and prediction layer, initialize the weight coefficient of the multi-objective function and the genetic algorithm parameters (population size, iteration number, crossover and mutation probability, etc.); 、 、 、 Load process constraint conditions and initial parameters of penalty function 、 、 ; (Two) objective function calculation stage: According to the current process parameters and prediction data, calculate the multi-objective function value, and process the individuals that violate the constraints combined with the penalty function to get the fitness value; (Three) genetic algorithm iteration optimization stage: Selection: adopt roulette or tournament selection strategy to select individuals from the population for genetic operation; Crossover and mutation: generate offspring individuals through single-point crossover or multi-point crossover, and introduce new gene combinations by using adaptive chaotic mutation operator; Constraint processing and evaluation: calculate the fitness of offspring individuals, update the non-dominated frontier and crowding degree, and select better individuals; Iteration termination judgment: when the maximum iteration number is reached or the convergence condition is met, stop iteration and output the Pareto optimal solution set; (Four) optimal solution screening and output stage: Use entropy-TOPSIS method to evaluate the Pareto solution set, calculate the closeness degree of each scheme and sort them; Transfer the optimal process parameter combination to the dynamic control layer, and store it to the process knowledge graph to complete one optimization cycle; (Five) end stage: When the system stops running or receives a stop instruction, save the parameter configuration, historical solution set and evaluation results in the optimization process, and release system resources; Five, the application value of the module: (One) realize the coordinated optimization of production indicators: By multi-objective function and intelligent algorithm, the impurity content is reduced by 15%-20%, the crystal yield is increased by 5%-8%, the unit energy consumption is reduced by 10%-15%, and the product competitiveness and production efficiency are significantly improved; (II) Enhance process adaptability and robustness: The adaptive chaos mutation and constraint processing mechanism enables the algorithm to quickly adapt to complex working conditions such as raw material fluctuations and equipment differences, generating feasible solutions that meet actual production needs; (III) Improve decision-making efficiency and scientificity: The entropy weight-TOPSIS evaluation method is based on data-driven decision-making, avoiding the limitations of human experience, and combining the optimization results with the process knowledge graph to support fast parameter recommendation and shorten the process debugging cycle; (IV) Promote green production: Through optimization of the energy minimization objective, the energy consumption and carbon emissions of the silver nitrate crystallization process are reduced, meeting the needs of sustainable development and helping enterprises achieve green manufacturing transformation.

[0026] In this embodiment, the dynamic control layer further includes: Abnormal condition processing unit: when the predicted impurity content exceeds 1 ppm, automatically trigger the dosing instructions of silver oxide / aluminum nitrate compound or nitric acid modified activated carbon; Closed-loop feedback unit: based on the kettle bottom temperature, real-time correction of the concentration endpoint judgment logic, control the temperature error ≤±2℃; Further, the dynamic control layer, as the "execution terminal" of the silver nitrate crystallization impurity content real-time prediction and process parameter multi-objective optimization system, undertakes the key task of converting the theoretical scheme generated by the multi-objective optimization layer into actual production control instructions; it ensures the stable operation of the silver nitrate crystallization process under complex conditions through real-time response, precise execution and intelligent feedback; the following is a comprehensive and in-depth exposition of the dynamic control layer from the aspects of functional positioning, core modules, technical principles, work processes and application value: I. Overall function overview: The dynamic control layer adjusts the temperature of the crystallization kettle, the stirring rate, the cooling curve, and the amount of additive added in real time based on the optimal process parameter combination output by the multi-objective optimization layer; at the same time, this layer has the ability to monitor and respond to abnormal conditions, and through a closed-loop feedback mechanism, it corrects control deviations to ensure that process parameters always remain in the optimal range; in addition, the dynamic control layer also integrates local storage and transmission of process data, providing data support for continuous optimization and fault tracing of the system; II. Submodule composition and function: (I) Real-time parameter adjustment unit: Temperature and cooling curve control: According to the optimization instructions, the operation power of the crystallizer heating / cooling equipment (such as electric heater, cooling liquid circulating pump) is adjusted by PID (proportional-integral-derivative) controller or model predictive control (MPC) algorithm; for example, when the optimization result requires the crystallization temperature to be maintained at 125°C, the controller collects the temperature sensor data in the kettle in real time, calculates the output control signal through the algorithm, dynamically adjusts the heating power, and ensures that the temperature fluctuation range is controlled within ±1°C; at the same time, according to the cooling curve optimization scheme, the cooling rate during the crystallization process is accurately controlled, such as cooling at a rate of 25°C / hour in the later stage of crystallization, to ensure the uniformity of crystal particle size; Stirring rate adjustment: The speed of the stirring motor is adjusted by a frequency converter to operate within the process constraint range of 60-80 revolutions / minute; combined with the fluid mixing demand predicted by the crystallization phase transition kinetics model, the stirring rate is dynamically adjusted; for example, in the early stage of crystallization, the stirring rate is set to 70 revolutions / minute to promote uniform distribution of solutes; in the crystal growth stage, the speed is appropriately reduced to 65 revolutions / minute to reduce damage to the crystal structure; Precise addition of additives: A high-precision metering pump or peristaltic pump is used to control the addition amount of impurity removal agent (silver oxide / aluminum nitrate complex or nitric acid modified activated carbon); when the optimization layer determines that the impurity removal agent needs to be added, the control unit calculates the addition volume according to the preset molar ratio (6-10:1) or concentration (2-8 grams / liter), and sends a pulse signal to the metering pump to realize precise addition of the additive, with an error controlled within ±5%; (II) Abnormal condition handling unit: Impurity content over-limit response: When the predicted impurity content exceeds the threshold value (>1 ppm), the composite impurity removal strategy is triggered; the system first determines the type of impurities, and if metal ion impurities are dominant, the silver oxide / aluminum nitrate complex addition program is automatically started to remove impurities by generating insoluble precipitates through chemical reaction; if the proportion of organic impurities is high, nitric acid modified activated carbon is added for adsorption treatment; at the same time, the occurrence time of abnormal events, impurity types and treatment measures are recorded to provide data for subsequent process improvement; Emergency treatment of equipment failure: The running state of key equipment (such as sensors, pumps, motors) is monitored in real time, and abnormal signals of the equipment are collected by vibration sensors, current sensors, etc.; when equipment failure (such as overload of stirring motor, interruption of sensor signal) is detected, an alarm is triggered immediately and fault isolation operation is performed; for example, the power supply line of the faulty sensor is cut off to prevent false data from interfering with system operation; at the same time, the standby equipment (such as standby sensor, redundant stirring motor) is started to ensure uninterrupted production process; (III) Closed-loop feedback correction unit: Dynamic correction of process parameters: Based on real-time data from the bottom temperature sensor at the crystallization endpoint, the system compares this data with the optimized target temperature and uses a closed-loop feedback algorithm to correct the concentration endpoint judgment logic. For example, when the actual bottom temperature deviates from the target temperature by more than ±2℃, the system automatically adjusts the operating parameters of the heating / cooling equipment or corrects the timing of additive addition to ensure the crystallization process proceeds as expected. The feedback control algorithm uses the following formula: (in, for Time-based control output; This is the initial control variable; , , These are the proportional, integral, and differential coefficients, respectively. for The error at time, i.e., the difference between the target value and the actual value); Data-driven control optimization: The actual process parameters and product quality data (impurity content, crystal yield) of each crystallization process are compared and analyzed with the optimization target; machine learning algorithms (such as reinforcement learning) are used to establish a mapping relationship between the control strategy and the process results, and the closed-loop feedback control parameters (such as PID coefficients) are automatically adjusted to achieve self-optimization of the control strategy and improve the stability and adaptability of the system in long-term operation. (iv) Data storage and transmission unit: Local data storage: Industrial-grade databases (such as MySQL and InfluxDB) are used to store real-time process parameters, equipment operating status, and abnormal event records. The data storage frequency is consistent with the sensor sampling frequency (e.g., temperature data is stored once per second), and a data backup mechanism is set up to ensure data security. The stored data can be used for process traceability, fault analysis, and model optimization. Remote data transmission: Through communication technologies such as industrial Ethernet and 5G, key process data (such as real-time temperature and predicted impurity content) are uploaded to the cloud management platform; at the same time, configuration update instructions (such as optimizing model parameters and adjusting control strategies) are received from the cloud to realize remote monitoring and intelligent upgrades of the system. III. Key Technology Principles: (I) Closed-loop control theory: Based on the principle of negative feedback, the system output (such as actual temperature and impurity content) is compared with the target value, and the input (such as heating power and additive dosage) is adjusted by the controller to form a closed loop of "detection-comparison-control" to ensure that the process parameters are stable within the set range. (II) Abnormal Operating Condition Diagnosis and Handling Technology: Combine sensor data with machine learning algorithms (such as support vector machines, LSTM) to build a fault diagnosis model, and through the analysis of the feature vector of the equipment operation data, realize the rapid identification of fault type; the abnormal processing strategy is based on the pre-set rule base and expert experience, to ensure that the fault is isolated and production is restored in the shortest time; (Three)Data-driven control optimization technology: Use historical production data to train reinforcement learning model, treat control strategy as agent's behavior, and treat process results (such as crystal yield, impurity content) as reward signal, through continuous trial and error learning, optimize control strategy, realize the transformation from experience control to data-driven control; Four, the working process of the module: (One)Initialization stage: Receive the optimal process parameter combination output by the multi-objective optimization layer, load PID controller parameters, abnormal handling rule base and other configuration information; Establish communication connection with data acquisition layer and cloud platform, initialize local database table structure; (Two)Real-time parameter regulation stage: According to the optimization instruction, adjust the temperature of the crystallization kettle, the stirring rate and the additive dosage through the actuator (frequency converter, metering pump, etc.); Real-time acquisition of process parameter feedback data, comparison with target value, fine-tuning of control parameters through closed-loop feedback algorithm, to ensure that the deviation between actual parameters and optimization value is within the allowable range; (Three)Abnormal condition monitoring and processing stage: Continuously monitor key indicators such as impurity content prediction value and equipment operating state, and when an abnormality (such as impurity exceeding standard, equipment failure) is detected, trigger an alarm and start the corresponding processing process; Execute fault isolation, standby equipment switching, emergency addition of impurity removal agent, etc., and record abnormal event information to the local database; (Four)Data storage and transmission stage: Store real-time process parameters, control instructions, abnormal event data, etc. to the local database, and establish index according to time sequence; Regularly package data and upload to the cloud platform, receive control strategy update instructions issued by the cloud, and update local configuration parameters; (Five)End stage: When the system stops running or receives a stop instruction, save the current control parameters, database state, close the communication connection with other modules, and release system resources; Five, the application value of the module: (One)Ensure production stability and product quality: Through precise parameter control and rapid response to abnormalities, impurity content fluctuations are controlled within a very small range, ensuring that silver nitrate products meet high-end application standards (such as electronic-grade and pharmaceutical-grade), while reducing the rate of defective products caused by process fluctuations and improving product consistency. (2) Reduce production costs and energy consumption: Closed-loop feedback control and intelligent optimization strategies enable fine management of energy and raw materials. Compared with traditional control methods, energy consumption per unit of product can be reduced by 10%-15%, waste of impurity removal agents is reduced, and production costs are directly reduced. (3) Improve production safety and reliability: The abnormal condition handling unit can take measures at the first time of equipment failure or process abnormality to prevent accidents from expanding and ensure production safety. At the same time, redundant device switching and data backup mechanisms ensure continuous operation of the system in emergency situations, improving overall reliability. (4) Support intelligent upgrading and process innovation: Data storage and transmission functions provide rich data resources for process optimization. Combined with cloud analysis and machine learning techniques, control strategies can be continuously optimized to promote the continuous upgrading of silver nitrate crystallization processes towards intelligence and automation, and to provide support for enterprise technological innovation.

[0027] In this embodiment, the system output end integrates a process knowledge graph, stores historical optimization solutions, impurity suppression strategies, and energy consumption mapping relationships, and supports case-based reasoning for rapid recommendation of process parameters. The process knowledge graph is based on ontology modeling and uses a triple (entity-relation-entity) structure to build a knowledge network. Core entities include: Process parameter entities: crystallization temperature, stirring rate, cooling curve, additive dosage, etc. Quality index entities: impurity content (iron, copper, lead, manganese, etc.), crystal yield, particle size distribution Energy consumption index entities: energy consumption per unit of production, device operating power consumption Optimization strategy entities: non-dominated sorting genetic algorithm parameters, multi-objective function weights, impurity removal agent formulations Historical case entities: record complete process data and optimization results for each crystallization batch The relationships between entities are defined through data mining algorithms and expert experience, for example: Causal relationship: "increasing stirring rate → reducing temperature gradient → reducing impurity wrapping rate" Correlation relationship: "correspondence between raw silver purity and impurity suppression strategy" Optimization mapping relationship: "process parameter combination → quality and energy consumption index changes"

[0028] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A system for real-time prediction of impurity content in silver nitrate crystals and multi-objective optimization of process parameters, characterized in that: include: Data acquisition layer: Integrates an online sensor network to collect multi-source heterogeneous data in real time during the silver nitrate crystallization process, including crystallizer temperature, stirring rate, solution density, mother liquor ion concentration, raw material silver purity, and real-time impurity element content data detected by inductively coupled plasma atomic emission spectrometry / mass spectrometry; Analysis and prediction layer: The impurity content dynamic prediction model based on deep learning takes real-time data from the data acquisition layer as input and outputs the predicted content and confidence intervals of iron, copper, lead and manganese impurity elements. The crystallization phase transformation kinetics model is integrated to correlate process parameters with crystal morphology, grain size distribution and impurity encapsulation effect; Multi-objective optimization layer: A multi-objective function is established with the optimization objectives of minimizing impurity content, maximizing crystal yield, and minimizing energy consumption. An improved non-dominated sorting genetic algorithm with constraints is used to generate the Pareto optimal solution set; Dynamic control layer: Based on the optimized solution set, process parameter adjustment instructions are generated to control the crystallizer temperature, stirring rate, cooling curve and additive dosage in real time.

2. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The dynamic prediction model for impurity content includes: The time-series feature extraction module uses an improved adaptive gated loop unit to process time-series data on temperature, stirring rate, and density; its hidden state update process is as follows: The update gate and reset gate are calculated using the input vector and the hidden state from the previous moment. Generate candidate hidden states based on the reset gate and the input vector; The current hidden state is generated by combining the update gate, candidate hidden states, and the previous hidden state. A process feature mask matrix is ​​introduced to enhance the influence of key parameters, and an adaptive weight distribution is calculated through a scaled dot product attention mechanism. Composition association module: Models the interactions of iron, copper, lead, and manganese impurity elements using a graph convolutional neural network; the node feature update process is as follows: Add self-joins to the adjacency matrix and normalize it; The normalized adjacency matrix and the current node feature matrix are linearly transformed and then superimposed with the metal ion bond energy correlation matrix; The updated node features are output using a nonlinear activation function; Dynamic correction module: Updates model weights based on real-time detection data.

3. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The multi-objective function is defined as the minimum weighted sum of total impurity content, the reciprocal of crystal yield, and energy consumption; the weight coefficients of the multi-objective function are dynamically adjusted according to the purity of the raw material silver; its constraints include a crystallization temperature range of 110℃ to 130℃ and a stirring rate range of 60 rpm to 80 rpm.

4. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The improved non-dominated sorting genetic algorithm includes: Adaptive chaotic mutation operator: In the early stages of algorithm iteration, the mutation probability is dynamically calculated using the first chaotic control parameter. In the later stages of the iteration, the mutation probability is dynamically calculated using a second chaotic control parameter. The iteration threshold is set to 40% of the total number of iterations. Process constraint processing module: A double penalty function is used to handle constraint violation problems; Calculate the square penalty term for inequality constraints and the absolute value penalty term for equality constraints; The penalty coefficient is adjusted in conjunction with the population diversity index; Solution set evaluation module: The optimal parameter combination is selected by using the entropy weight method and the approximation ideal solution sorting method.

5. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The dynamic control layer also includes: Abnormal operating condition handling unit: When the predicted impurity content exceeds 1ppm, it automatically triggers the addition command of silver oxide / aluminum nitrate composite or nitric acid modified activated carbon. Closed-loop feedback unit: Based on the bottom temperature of the vessel, the concentration endpoint judgment logic is corrected in real time to control the temperature error ≤ ±2℃.

6. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The data acquisition layer uses thiourea complexation-inductively coupled plasma mass spectrometry to detect 21 impurity elements in silver; the detected elements include copper, bismuth, iron, lead, gold, and platinum, with a detection limit of ≤0.00001%.

7. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 2, characterized in that: The update strategy of the dynamic correction module is as follows: After each batch of crystallization is completed, the predicted values ​​are compared with the actual test data; If the prediction deviation exceeds 5% for three consecutive batches, the transfer learning mechanism is activated to fine-tune the parameters of the fully connected layer of the model.

8. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 1, characterized in that: The system output integrates a process knowledge graph, storing historical optimized solutions, impurity suppression strategies, and energy consumption mapping relationships, supporting rapid recommendation of process parameters based on case-based reasoning.

9. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 2, characterized in that: The kinetic model of crystallization phase transformation includes: The rate of change of crystallization conversion is directly proportional to the temperature-dependent rate constant; It exhibits an exponential relationship with the unconverted proportion and the logarithmic transformation term; The temperature gradient norm is introduced as a quantitative characterization of the stirring rate, and the impurity encapsulation effect is adjusted by an influencing factor.

10. The system for real-time prediction of impurity content and multi-objective optimization of process parameters in silver nitrate crystallization according to claim 4, characterized in that: The penalty coefficient adjustment rule is as follows: The inequality constraint penalty coefficient increases linearly with the population diversity index; The equality constraint penalty coefficient decreases exponentially with the population diversity index; When population diversity declines, priority should be given to ensuring that hard constraints of the process are met.

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