Intelligent recovery treatment method and system for titanium dioxide production waste acid

Abstract

The invention discloses an intelligent recovery processing method and system for titanium dioxide production waste acid, and relates to the technical field of waste acid treatment.The intelligent recovery processing method comprises the steps that a plurality of process nodes of a titanium dioxide production process are obtained, and a plurality of waste acid sample data sets corresponding to the process nodes are collected; setting a plurality of pipeline systems according to the plurality of process nodes; a collaborative recovery processing module is arranged, and the collaborative recovery processing module comprises a mixed recovery processing module and a single recovery processing module; inputting the plurality of waste acid sample data sets into a recovery risk analysis model to divide a plurality of process nodes, and marking the process nodes as mixed recovery processing nodes and single recovery processing nodes; the mixed recycling treatment module is used for recycling the waste acid of the mixed recycling treatment node, and the single recycling treatment module is used for recycling the waste acid of the single recycling treatment node. The technical problems that waste acid cannot be classified and treated in the prior art, and the waste acid treatment efficiency is low are solved, and the technical effect of improving the waste acid treatment efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste acid treatment, and in particular to an intelligent recovery and treatment method and system for waste acid in titanium dioxide production. Background Art

[0002] With the expansion of industrial production and increasingly stringent environmental protection requirements, waste acid treatment in titanium dioxide production has become a key link in the production process. Traditional waste acid treatment methods mainly rely on a single treatment mode, usually using neutralization, sedimentation or simple physical recovery. However, with the complexity of titanium dioxide production processes, the composition and characteristics of waste acids have become more diverse and complex, which poses a huge challenge to the efficient treatment of waste acids. In the prior art, waste acid treatment systems often lack flexibility and cannot be classified and treated according to the different components and treatment requirements of waste acids, resulting in low treatment efficiency. Summary of the invention

[0003] The present application provides an intelligent recovery and treatment method and system for waste acid in titanium dioxide production, which is used to solve the technical problem that the prior art is unable to classify and treat waste acid and has low waste acid treatment efficiency.

[0004] In view of the above problems, the present application provides an intelligent recovery and treatment method and system for waste acid in titanium dioxide production.

[0005] In a first aspect of the present application, a method for intelligently recovering and treating waste acid from titanium dioxide production is provided, the method comprising:

[0006] Acquire multiple process nodes of the titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes; set up multiple pipeline systems according to the multiple process nodes, wherein each pipeline system is used to collect waste acid of the corresponding process node, and a diverter valve is set at the outlet of each pipeline, and the path of the waste acid in the pipeline is changed according to the diverter valve; set up a collaborative recovery and processing module, and the collaborative recovery and processing module includes a mixed recovery and processing module and a single recovery and processing module; input the multiple waste acid sample data sets into a recovery risk analysis model, divide the multiple process nodes according to the recovery risk analysis model, and mark them as mixed recovery and processing nodes and single recovery and processing nodes; the mixed recovery and processing module recovers the waste acid of the mixed recovery and processing node, and the single recovery and processing module recovers the waste acid of the single recovery and processing node.

[0007] The second aspect of the present application provides an intelligent recovery and treatment system for waste acid in titanium dioxide production, the system comprising:

[0008] A process node acquisition unit, the process node acquisition unit is used to acquire multiple process nodes of the titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes; a pipeline system setting unit, the pipeline system setting unit is used to set multiple pipeline systems according to the multiple process nodes, wherein each pipeline system is used to collect waste acid of the corresponding process node, and a diverter valve is provided at the outlet of each pipeline, and the path of the waste acid in the pipeline is changed according to the diverter valve; a processing module setting unit, the processing module setting unit is used to set a collaborative recovery processing module, and the collaborative recovery processing module includes a mixed recovery processing module and a single recovery processing module; a node division unit, the node division unit inputs the multiple waste acid sample data sets into a recovery risk analysis model, divides the multiple process nodes according to the recovery risk analysis model, and identifies them as mixed recovery processing nodes and single recovery processing nodes; a recovery processing unit, the recovery processing unit is used for the mixed recovery processing module to recycle the waste acid of the mixed recovery processing node, and the single recovery processing module to recycle the waste acid of the single recovery processing node.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application obtains multiple process nodes of the titanium dioxide production process, collects multiple waste acid sample data sets corresponding to the multiple process nodes; multiple pipeline systems are set according to the multiple process nodes, wherein each pipeline system is used to collect the waste acid of the corresponding process node, and the outlet of each pipeline is provided with a steering valve, and the path of the waste acid in the pipeline is changed according to the steering valve; a collaborative recovery and processing module is set, and the collaborative recovery and processing module includes a mixed recovery and processing module and a single recovery and processing module; multiple waste acid sample data sets are input into a recovery risk analysis model, and multiple process nodes are divided according to the recovery risk analysis model, and are marked as mixed recovery and processing nodes and single recovery and processing nodes; the mixed recovery and processing module recovers the waste acid of the mixed recovery and processing node, and the single recovery and processing module recovers the waste acid of the single recovery and processing node. The present invention solves the technical problem that the prior art cannot classify and process waste acid, and there is a low waste acid treatment efficiency. By collecting data and performing risk analysis on waste acid samples of multiple process nodes in titanium dioxide production, waste acid is classified into two types suitable for mixed recovery and processing and single recovery and processing, and the set collaborative recovery and processing module is used to carry out targeted treatment of different waste acids, so as to achieve the technical effect of improving the waste acid treatment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic flow chart of an intelligent recovery and treatment method for waste acid in titanium dioxide production provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of an intelligent recovery and treatment system for waste acid in titanium dioxide production provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: process node acquisition unit 11 , pipeline system setting unit 12 , processing module setting unit 13 , node division unit 14 , recycling processing unit 15 . DETAILED DESCRIPTION

[0015] The present application provides an intelligent recovery and treatment method and system for waste acid in titanium dioxide production, aiming to solve the technical problem that the existing technology is unable to classify and treat waste acid and has low waste acid treatment efficiency. By collecting data and performing risk analysis on waste acid samples from multiple process nodes in titanium dioxide production, the waste acid is classified into two types suitable for mixed recovery and treatment and separate recovery and treatment. The set collaborative recovery and treatment module is used to carry out targeted treatment of different waste acids, thereby achieving the technical effect of improving the waste acid treatment efficiency.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent recovery and treatment method for waste acid in titanium dioxide production, the method comprising:

[0019] Step S100: Acquire multiple process nodes of a titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes.

[0020] In the embodiment of the present application, firstly, multiple key process nodes are determined according to the titanium dioxide production process. These process nodes include acid hydrolysis, precipitation, filtration, washing and drying stages. At each process node, appropriate waste acid sample collection equipment is installed, including sensors, samplers and data recording devices, to monitor and collect waste acid samples in real time. The sensor is used to detect key parameters such as the composition, concentration, temperature, pH value, etc. of the waste acid; the sampler is used to extract representative waste acid samples; the data recording device is used to store and transmit the collected waste acid sample data.

[0021] Once the equipment is installed, waste acid samples are automatically collected at preset time intervals to obtain multiple waste acid sample data sets corresponding to multiple process nodes. The waste acid sample data sets include waste acid composition data, acid concentration, metal ion content, and organic matter content.

[0022] Step S200: multiple pipeline systems are set according to the multiple process nodes, wherein each pipeline system is used to collect waste acid of the corresponding process node, and a diverter valve is set at the outlet of each pipeline, and the path of the waste acid in the pipeline is changed according to the diverter valve.

[0023] In the embodiment of the present application, each process node will be separately provided with a pipeline system, which is specifically used to collect the waste acid generated by the node. When setting up multiple pipeline systems, first select appropriate pipeline materials and specifications according to the characteristics of the waste acid, such as acid concentration, corrosiveness, flow rate, etc., such as corrosion-resistant PVC or stainless steel pipelines, to ensure that the pipeline can withstand the chemical erosion and flow pressure of the waste acid. Then, based on the spatial layout of the production process, plan the layout of the pipeline system, accurately connect the inlet of the pipeline to the outlet of the reactor of the corresponding process node, so that the waste acid enters the dedicated pipeline system directly from the specific node in the production process, and realizes accurate collection.

[0024] At the outlet of each pipeline system, a diverter valve is installed. The function of the diverter valve is to dynamically switch the flow path of the waste acid according to the treatment requirements during the flow of the waste acid. By adjusting the diverter valve, the waste acid is directed to different treatment modules, such as a mixed recovery treatment module or a single recovery treatment module.

[0025] Furthermore, in the method provided in the embodiment of the application, multiple pipeline systems are set according to the multiple process nodes, and further include:

[0026] Among them, the pipeline inlet of each pipeline system is connected to the reactor outlet of the corresponding process node, and the pipeline outlet includes a first outlet and a second outlet, the first outlet is connected to the reactor inlet of the mixed recovery processing module, and the second outlet is connected to the reactor inlet of the single recovery processing module; according to the change of the path of the waste acid in the pipeline by the diverter valve, it flows into the mixed reactor of the mixed recovery processing module or the single reactor of the single recovery processing module.

[0027] In the embodiment of the present application, the pipeline inlet of each pipeline system is directly connected to the reactor outlet of the corresponding process node, ensuring that the waste acid generated at the node can smoothly enter the pipeline system for collection and transportation.

[0028] The pipeline outlet of the pipeline system is divided into two parts: the first outlet and the second outlet. The first outlet is connected to the reactor inlet of the mixed recovery treatment module, and is used to guide the waste acid that meets the mixed treatment conditions to the mixed recovery treatment module for treatment; while the second outlet is connected to the reactor inlet of the single recovery treatment module, and is specifically used to treat the waste acid that needs to be treated separately.

[0029] In order to flexibly control the treatment path of waste acid during the production process, a steering valve is installed at the outlet of each pipeline system. By adjusting the steering valve, the flow path of the waste acid in the pipeline is changed. Specifically, when it is detected that the waste acid generated at a certain process node is suitable for mixed recovery treatment, the steering valve switches the flow direction of the waste acid to the first outlet, allowing the waste acid to flow into the mixed reactor of the mixed recovery treatment module; when the waste acid needs to be treated separately, the steering valve switches the flow direction to the second outlet, and the waste acid is introduced into the single reactor of the single recovery treatment module for treatment.

[0030] Step S300: Setting a collaborative recycling processing module, wherein the collaborative recycling processing module includes a mixed recycling processing module and a single recycling processing module.

[0031] In an embodiment of the present application, in order to effectively treat and recycle the waste acid generated at each process node, a collaborative recovery and processing module is set up, which includes a mixed recovery and processing module and a single recovery and processing module to meet the processing requirements of waste acids with different characteristics.

[0032] The mixed recovery treatment module is mainly used to treat mixed waste acid from multiple process nodes. It contains one or more mixed reactors for treating waste acid with complex components and suitable for joint treatment under a unified reaction environment. The module also integrates a multi-stage separation device, including filters, settling tanks and evaporators. Specifically, the filter is used to remove solid impurities in the waste acid, and the settling tank is used to precipitate and separate suspended particles. Since heavy metal ions and other dissolved ions in the waste acid cannot be removed by sedimentation, further chemical treatment steps such as ion exchange, chemical precipitation or membrane separation technology are used in the settling tank to remove ions. Finally, the remaining acid is concentrated by the evaporator to recover reusable substances.

[0033] The single recovery module is specially used to treat waste acids that need to be treated separately. These waste acids may need to be treated separately due to their special composition or potential cross-contamination risks. The module contains one or more single reactors, which are configured according to the specific characteristics of the waste acid to ensure the effectiveness and safety of the treatment process. In addition, the single recovery module is also equipped with an acid regeneration system to restore the treated acid to the initial acid concentration. The regenerated acid can be recycled in the titanium dioxide production process, thereby achieving effective utilization of resources.

[0034] Furthermore, the method provided in the application embodiment also includes:

[0035] The mixed recovery treatment module includes at least one mixing reactor and a multi-stage separation device, and the multi-stage separation device is used to perform multi-stage separation on the waste acid after the mixed recovery treatment; wherein, the multi-stage separation device includes a filter, a sedimentation tank and an evaporator, the filter is used to preliminarily remove solid impurities, the sedimentation tank is used to precipitate and separate heavy metal ions, and the evaporator is used to concentrate and recover the remaining acid liquid.

[0036] In an embodiment of the present application, the mixed recovery processing module includes at least one mixing reactor and a multi-stage separation device to ensure that the waste acid is fully treated and effectively recovered.

[0037] When the waste acid is treated and recycled, the waste acid is first collected from each process node into a mixing reactor. Here, the waste acid from different process nodes is mixed and treated under uniform reaction conditions. The treated waste acid enters a multi-stage separation device, which consists of a filter, a settling tank and an evaporator to achieve multiple purifications and material recovery of the waste acid. Specifically, the waste acid flows through the filter for preliminary treatment to remove solid impurities in the waste acid, such as particulate matter or unreacted residues. The function of the filter is to ensure that the subsequent separation steps can be carried out in a relatively pure liquid environment, thereby improving the separation efficiency. Next, the filtered waste acid enters the settling tank, where the suspended particulate matter in the waste acid is separated by sedimentation. Since the heavy metal ions in the waste acid are usually present in a dissolved state and cannot be removed by sedimentation, the dissolved heavy metal ions are removed by chemical precipitation in the settling tank. Specifically, a suitable precipitant, such as sodium hydroxide, is added to the waste acid. When sodium hydroxide reacts with heavy metal ions in waste acid, such as nickel, copper or zinc, a water-insoluble metal hydroxide precipitate is generated. After the reaction is completed, the precipitate is separated from the waste acid by gravity sedimentation or centrifugal separation. Finally, the waste acid enters the evaporator, which concentrates the remaining liquid in the waste acid by evaporation and further recovers valuable acid. The evaporator uses the principle of heating and decompression to evaporate the water in the waste acid, thereby increasing the concentration of the remaining acid. These high-concentration acids can be further recovered and used in the production process, forming resource reuse.

[0038] Through the above steps, the mixed recovery treatment module performs efficient multi-stage separation on the waste acid after mixed recovery treatment, thereby removing impurities, separating heavy metals, and recovering valuable acid liquid.

[0039] Furthermore, the method provided in the application embodiment also includes:

[0040] The single recovery processing module includes at least one single reactor and an acid regeneration system; the single reactor is used to adapt the waste acid treatment type according to the waste acid sample data set in the single recovery processing node, and perform waste acid recovery processing according to the adaptation result; the acid regeneration system is used to restore the acid after the single recovery treatment to the initial acid concentration, and circulate it into the titanium dioxide production process.

[0041] In the embodiment of the present application, during the production of titanium dioxide, the waste acid generated at certain process nodes must be treated separately due to its complex or special composition. In this case, it is treated separately by a single recovery and processing module. The single recovery and processing module includes at least one single reactor and an acid regeneration system, which achieve the treatment of waste acid and the regeneration of acid through close cooperation.

[0042] Specifically, when a process node detects that a waste acid sample needs to be treated separately, the waste acid is introduced into a single reactor, and key data, including acid concentration, metal ion content, organic matter ratio, etc., are obtained from the waste acid sample data set. The intelligent control system of the single reactor analyzes these data to identify the main components and treatment requirements of the waste acid. According to the analysis results, the most appropriate waste acid treatment type is automatically adapted. For example, if the waste acid sample shows high concentrations of titanium ions and sulfate ions, a precipitation reaction is selected to separate and treat these components, thereby reducing the harm of the waste acid. After the treatment adaptation is completed, the single reactor performs the corresponding operation according to the selected treatment type. For example, if the adaptation result shows that neutralization treatment is required, the reactor will automatically adjust the pH value and add an appropriate amount of neutralizer to effectively neutralize the acidic components in the waste acid. After the treatment is completed, the harmful substances in the waste acid are neutralized or precipitated, and the remaining acid is transported to the acid regeneration system. In this regeneration system, the waste acid is further treated to restore its initial acid concentration. For example, the acid is first heated to a specific temperature for evaporation and concentration, and excess water is removed to gradually increase the concentration of the acid. Then, the residual impurities are removed by filtration and distillation technology to ensure that the acid is pure and pollution-free. Finally, the concentration of the acid is adjusted by adding an appropriate amount of supplementary acid to meet the production process requirements. The regenerated acid is not discarded, but is transported back to the titanium dioxide production process through a dedicated pipeline to continue to participate in the next round of production process.

[0043] Step S400: inputting the plurality of waste acid sample data sets into a recovery risk analysis model, and dividing the plurality of process nodes according to the recovery risk analysis model, and identifying them as mixed recovery processing nodes and single recovery processing nodes.

[0044] In the embodiment of the present application, multiple waste acid sample data sets of each process node are input into the recycling risk analysis model. The model is used to conduct risk assessment on the waste acid samples, thereby dividing the process nodes and identifying which nodes are suitable for mixed recycling and which nodes need to be recycled separately.

[0045] Specifically, multiple waste acid sample data sets are input into the recycling risk analysis model, and the recycling risk analysis model performs high-dimensional risk feature mapping analysis on each waste acid sample data set. By using methods such as RBF (radial basis function) kernel function, the waste acid sample data is mapped to a high-dimensional feature space to reveal the potential risk characteristics of the waste acid. The model generates a series of risk indicators, such as pollutant residue indicators, waste acid recovery leakage, reactant hazard, and recovery material damage. After obtaining these risk indicators, the waste acid of each process node is classified according to the preset processing risk threshold. If the risk indicator of the waste acid sample data set of a certain process node is greater than or equal to the preset processing risk threshold, the node is identified as a single recycling processing node, which means that the waste acid generated by the node needs to be recycled and processed separately to avoid the safety or pollution risks that may be caused by mixed processing. If the risk indicator is lower than the preset processing risk threshold, the process node is identified as a mixed recycling processing node, indicating that the waste acid of the node can be mixed with the waste acid of other nodes for processing, thereby improving resource utilization efficiency.

[0046] Furthermore, in the method provided in the embodiment of the application, the plurality of waste acid sample data sets are input into the recovery risk analysis model, and further includes:

[0047] Acquire the multiple waste acid sample data sets, wherein each waste acid sample data set includes composition data, acid concentration, metal ion content and organic matter content; perform high-dimensional risk feature mapping analysis on each of the multiple waste acid sample data sets to obtain multiple processing risk indicators; set a preset processing risk according to the mixed recovery device of the mixed recovery processing module; judge the multiple processing risk indicators based on the preset processing risk indicator, identify the process nodes greater than or equal to the preset processing risk indicator as single recovery processing nodes, and identify the process nodes less than the preset processing risk indicator as mixed recovery processing nodes.

[0048] In the present application embodiment, in the titanium dioxide production waste acid treatment process, in order to accurately determine the waste acid treatment method of each process node, first obtain multiple waste acid sample data sets. Each data set contains the key component data of the waste acid, such as acid concentration, metal ion content and organic content. Next, each waste acid sample data set is subjected to high-dimensional risk feature mapping analysis. By using analysis methods such as RBF (radial basis function) kernel function, the waste acid sample data is mapped into a high-dimensional feature space. The purpose of this step is to extract the potential risk features in the waste acid sample, thereby generating a series of processing risk indicators. These risk indicators include the risk of residual pollutants, the possible leakage risk when the waste acid is recovered, the danger of reactants and the damage degree of the recovered materials, etc.

[0049] After obtaining multiple treatment risk indicators, a preset treatment risk threshold is set according to the capacity of the mixed recovery treatment module. This threshold is set according to the treatment capacity and safety standards of the mixed recovery unit to determine whether the waste acid is suitable for mixed recovery treatment.

[0050] All treatment risk indicators are then compared with the preset treatment risk threshold. For those process nodes whose risk indicators are greater than or equal to the preset treatment risk, they are identified as single recovery treatment nodes, which means that the waste acid generated by these nodes needs to be treated separately to avoid the safety or pollution risks that may arise when mixed with other waste acids. For those process nodes whose risk indicators are lower than the preset treatment risk, they are identified as mixed recovery treatment nodes, indicating that the waste acid from these nodes can be safely mixed with the waste acid from other nodes, thereby improving resource utilization efficiency.

[0051] Furthermore, in the method provided in the embodiment of the application, high-dimensional risk feature mapping analysis is performed on each of the multiple waste acid sample data sets, and further includes:

[0052] A high-dimensional risk feature mapping is performed on each of the multiple waste acid sample data sets through an RBF kernel function to obtain pollutant residue index, waste acid recovery leakage, reactant hazard, and recovery material damage degree; wherein the expression of the RBF kernel function is as follows:

[0053]

[0054] Among them, K(x i ,x j ) is the output value of the kernel function, sample x i and sample x j The inner product in high-dimensional space is used to measure the similarity between samples, x i is the feature vector of the i-th waste acid sample data set, x j is the feature vector of the jth waste acid sample data set, ||x i -x j || 2 For x i and sample x j The square of the Euclidean distance between them represents the distance between two samples in the feature space. σ is the width parameter of the kernel function, which controls the decay speed of the similarity between samples.

[0055] In the embodiment of the present application, each of the waste acid sample data sets in the multiple waste acid sample data sets is subjected to high-dimensional risk feature mapping by using the RBF kernel function. Specifically, the RBF kernel function maps the sample data into a high-dimensional space to calculate the similarity between each pair of samples. By calculating according to the formula given above, K(x i ,xj ) is the output value of the kernel function, that is, sample x i and sample x j Similarity in high-dimensional space. ||x i -x j || 2 For x i and sample x j The square of the Euclidean distance between two samples is used to measure the distance between two samples. σ is the width parameter of the kernel function, which controls the decay rate of the similarity between samples and is set by technical experts. The value of the kernel function width parameter σ has an important influence on the results. If σ is small, the kernel function will be more sensitive to the distance between samples, that is, the similarity will change sharply with the change of sample distance; if σ is large, the kernel function will be smoother, and the similarity will change slowly with the change of sample distance.

[0056] After completing the high-dimensional feature mapping, the key risk features related to waste acid treatment are extracted. First, by analyzing the organic matter and metal ion concentrations of waste acid samples in high-dimensional space, the model uses cluster analysis methods to group samples with similar characteristics and identify pollutants in these samples that may still be difficult to completely remove after treatment. This process clusters samples with similar pollution characteristics by calculating the distance and similarity between each sample, and finally extracts their pollutant residue indicators. Next, discriminant analysis is used to evaluate the possible leakage risk of waste acid during treatment or transportation. Specifically, based on the physicochemical properties of waste acid samples, the features of known high leakage risk samples are used as training sets, and supervised learning methods are used to identify waste acid samples with similar properties, thereby extracting their waste acid recovery leakage. This step determines which samples have similar leakage risks by calculating the similarity between samples and known high-risk samples in high-dimensional space.

[0057] At the same time, reaction kinetic analysis is used to evaluate the potential dangers of chemical reactions in the waste acid samples during the treatment process. For example, the dangerous reactions that may be caused by the interaction of different components in the sample are analyzed, and the probability that the sample may produce toxic gases or explosive substances is calculated by combining simulation or experimental data. In this way, samples with similar hazardous characteristics are captured, and the hazardous indicators of these reactants are extracted. Finally, regression analysis is used to evaluate the damage that waste acid may cause to recycling equipment or materials. Specifically, by analyzing the similarity of samples in high-dimensional feature space, a damage prediction model is established using historical data. By comparing the performance of similar samples in recycling equipment, it is predicted which waste acid samples may cause greater corrosion or physical damage to the recycled materials, and finally the damage characteristics are extracted.

[0058] Through the above process, multiple treatment risk indicators are extracted, such as pollutant residue indicators, leakage of waste acid recovery, hazardousness of reactants, and degree of damage to recovered materials.

[0059] Furthermore, the method provided in the application embodiment also includes:

[0060] Obtain an initialized support vector machine; train the initialized support vector machine according to the pollutant residue index, waste acid recovery leakage, reactant hazard, and recovery material damage until the training converges, and output a recovery risk analysis model; converge the recovery risk analysis model by minimizing the objective function, and the expression is as follows:

[0061]

[0062] Among them, min(w,b) represents the objective function to be minimized to obtain the best classification decision boundary, w is the weight vector, which represents the weight parameter of the classification model and is used to map the input feature vector to the classification decision parameter; b is the bias term, which represents the intercept parameter of the classification model and is used to adjust the position of the classification boundary, C is the regularization parameter, which is used to control the penalty for classification errors, m is the number of samples after convolution, and y is the weight parameter of the classification model. i is the label of the sample’s classification category, z i is the feature vector of the i-th sample, max(0,1-y i (w T z i +b) is the loss function. For each sample i, if the predicted value of the classifier is greater than or equal to 1, the loss is 0, otherwise the loss is 1-y i (w T z i +b).

[0063] In the embodiment of the present application, the support vector machine is first initialized. The support vector machine is a model that can generate a classification decision boundary through data training, and is used to classify waste acid sample data of different risk categories. At this stage, the initial state of the support vector machine is untrained, that is, the weight vector w and the bias term b of the model are randomly initialized.

[0064] Then, the initialized support vector machine is trained based on the extracted pollutant residue index, waste acid recovery leakage, reactant hazard, and recovery material damage. The training process is to continuously adjust the model parameters until the model can accurately classify the input sample data. Specifically, the goal of the support vector machine is to minimize an objective function to ensure that the model can achieve the best classification effect.

[0065] In the minimization objective function, min(w,b) represents the objective function to be minimized, and its purpose is to obtain the best classification decision boundary. The weight vector w represents the weight parameter of the classification model, which is used to map the input feature vector to the parameter of the classification decision. The bias term b represents the intercept parameter of the classification model, which is used to adjust the position of the classification boundary. The regularization parameter C is used to control the penalty for classification errors and balance the model complexity with classification errors. The number of samples m is the number of samples used for training, and the label y is i Indicates the classification category of the sample, usually +1 or -1. In the objective function, max(0,1-y i (w T z i +b) is the loss function. For each sample i, if the predicted value of the classifier is greater than or equal to 1, the loss is 0, otherwise the loss is 1-y i (w T z i +b). By minimizing this objective function, the support vector machine is able to find the optimal classification boundary, allowing the model to accurately classify the risk features associated with waste acid treatment.

[0066] Through repeated training, the support vector machine model will gradually converge and eventually output an optimized risk analysis model.

[0067] Step S500: the mixed recovery processing module recovers the waste acid of the mixed recovery processing node, and the single recovery processing module recovers the waste acid of the single recovery processing node.

[0068] In the embodiment of the present application, the recovery process is divided into two different treatment paths: mixed recovery treatment and single recovery treatment according to the classification of the waste acid.

[0069] For the waste acid divided into mixed recovery and treatment nodes, the mixed recovery and treatment module centrally treats these waste acids. Specifically, first, the waste acid is introduced into a mixed reactor from different process nodes for mixed treatment. The mixed reactor controls the conditions such as temperature, pH value and reaction time to make the harmful components in the waste acid undergo chemical reactions, thereby reducing its toxicity and pollutant concentration. The treated waste acid is then transported to a multi-stage separation device, which includes a filter, a sedimentation tank and an evaporator. The filter is used to remove solid impurities in the waste acid, the sedimentation tank separates heavy metal ions from the waste acid by gravity sedimentation, and the evaporator recovers the remaining acid liquid in the waste acid by evaporation and concentration. Through these steps, the mixed recovery and treatment module can effectively treat various mixed waste acids and make them meet the emission standards or recycling requirements.

[0070] For waste acids that are classified as single recovery nodes, the single recovery module adopts a more sophisticated treatment process. The single reactor in the single recovery module is specifically used to treat waste acids with complex components or high hazards. These waste acids cannot be mixed with other waste acids, so they need to be reacted separately. The single reactor neutralizes or removes harmful components according to the characteristics of the waste acid by adjusting the reaction conditions, such as adding specific chemical reagents or controlling the reaction time. The treated waste acid will be further processed and restored to a reusable state through the acid regeneration system, so that it can be put back into the titanium dioxide production process.

[0071] Through the above process, the waste acid is recovered and processed.

[0072] Furthermore, in the method provided in the embodiment of the application, after identifying the process node with a risk less than the preset processing risk index as a mixed recovery processing node, it also includes:

[0073] The time sequence of the mixed recovery processing nodes is obtained; taking the preset processing risk index as the target, the mixed recovery processing nodes are mixed clustered according to the time sequence, and the mixed clustering result is output; the mixed recovery processing module recycles the waste acid of the mixed recovery processing nodes according to the mixed clustering result.

[0074] In the embodiment of the present application, the time sequence corresponding to the identified mixed recovery processing nodes is first obtained. The time sequence refers to the time sequence of waste acid generation or waste acid flow of these nodes in the production process.

[0075] Next, with the preset processing risk indicators as the target, a hybrid clustering analysis is performed on these mixed recycling and processing nodes. Hybrid clustering combines multiple clustering techniques, such as the K-means algorithm and the Gaussian mixture model. The former achieves grouping by assigning nodes to the nearest cluster center, while the latter estimates model parameters through the expectation maximization (EM) algorithm, thereby handling data distribution more flexibly. In this process, the mixed recycling and processing nodes are classified and the hybrid clustering results are output according to the time sequence and risk indicators of the nodes. These results indicate which nodes have similar characteristics and processing requirements for waste acid and are suitable for mixed processing in the same batch. Finally, the mixed recycling processing module recycles the waste acid based on these hybrid clustering results, giving priority to those high-risk or time-sensitive nodes to ensure that the waste acid is treated in a timely and effective manner.

[0076] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0077] The present application obtains multiple process nodes of the titanium dioxide production process, collects multiple waste acid sample data sets corresponding to the multiple process nodes; multiple pipeline systems are set according to the multiple process nodes, wherein each pipeline system is used to collect the waste acid of the corresponding process node, and the outlet of each pipeline is provided with a steering valve, and the path of the waste acid in the pipeline is changed according to the steering valve; a collaborative recovery and processing module is set, and the collaborative recovery and processing module includes a mixed recovery and processing module and a single recovery and processing module; multiple waste acid sample data sets are input into a recovery risk analysis model, and multiple process nodes are divided according to the recovery risk analysis model, and are marked as mixed recovery and processing nodes and single recovery and processing nodes; the mixed recovery and processing module recovers the waste acid of the mixed recovery and processing node, and the single recovery and processing module recovers the waste acid of the single recovery and processing node. The present invention solves the technical problem that the prior art cannot classify and process waste acid, and there is a low waste acid treatment efficiency. By collecting data and performing risk analysis on waste acid samples of multiple process nodes in titanium dioxide production, waste acid is classified into two types suitable for mixed recovery and processing and single recovery and processing, and the set collaborative recovery and processing module is used to carry out targeted treatment of different waste acids, so as to achieve the technical effect of improving the waste acid treatment efficiency.

[0078] Example 2, based on the same inventive concept as the intelligent recovery and treatment method for waste acid in titanium dioxide production in the previous example, Figure 2 As shown, the present application provides an intelligent recovery and treatment system for waste acid in titanium dioxide production, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0079] A process node acquisition unit 11, the process node acquisition unit 11 is used to acquire multiple process nodes of the titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes; a pipeline system setting unit 12, the pipeline system setting unit 12 is used to set multiple pipeline systems according to the multiple process nodes, wherein each pipeline system is used to collect waste acid of the corresponding process node, and a diverter valve is provided at the outlet of each pipeline, and the path of the waste acid in the pipeline is changed according to the diverter valve; a processing module setting unit 13, the processing module setting unit 13 is used to set a collaborative recovery processing module, and the collaborative recovery processing module includes a mixed recovery processing module and a single recovery processing module; a node division unit 14, the node division unit 14 inputs the multiple waste acid sample data sets into a recovery risk analysis model, divides the multiple process nodes according to the recovery risk analysis model, and identifies them as mixed recovery processing nodes and single recovery processing nodes; a recovery processing unit 15, the recovery processing unit is used for the mixed recovery processing module to recycle the waste acid of the mixed recovery processing node, and the single recovery processing module to recycle the waste acid of the single recovery processing node.

[0080] Furthermore, the system is also used to implement the following functions:

[0081] Among them, the pipeline inlet of each pipeline system is connected to the reactor outlet of the corresponding process node, and the pipeline outlet includes a first outlet and a second outlet, the first outlet is connected to the reactor inlet of the mixed recovery processing module, and the second outlet is connected to the reactor inlet of the single recovery processing module; according to the change of the path of the waste acid in the pipeline by the diverter valve, it flows into the mixed reactor of the mixed recovery processing module or the single reactor of the single recovery processing module.

[0082] Furthermore, the system is also used to implement the following functions:

[0083] Acquire the multiple waste acid sample data sets, wherein each waste acid sample data set includes composition data, acid concentration, metal ion content and organic matter content; perform high-dimensional risk feature mapping analysis on each of the multiple waste acid sample data sets to obtain multiple processing risk indicators; set a preset processing risk according to the mixed recovery device of the mixed recovery processing module; judge the multiple processing risk indicators based on the preset processing risk indicator, identify the process nodes greater than or equal to the preset processing risk indicator as single recovery processing nodes, and identify the process nodes less than the preset processing risk indicator as mixed recovery processing nodes.

[0084] Furthermore, the system is also used to implement the following functions:

[0085] A high-dimensional risk feature mapping is performed on each of the multiple waste acid sample data sets through an RBF kernel function to obtain pollutant residue index, waste acid recovery leakage, reactant hazard, and recovery material damage degree; wherein the expression of the RBF kernel function is as follows:

[0086]

[0087] Among them, K(x i ,x j ) is the output value of the kernel function, sample x i and sample x j The inner product in high-dimensional space is used to measure the similarity between samples, x i is the feature vector of the i-th waste acid sample data set, x j is the feature vector of the jth waste acid sample data set, ||x i -x j || 2 For x i and sample x jThe square of the Euclidean distance between them represents the distance between two samples in the feature space. σ is the width parameter of the kernel function, which controls the decay speed of the similarity between samples.

[0088] Furthermore, the system is also used to implement the following functions:

[0089] Get the initialized support vector machine;

[0090] The initialized support vector machine is trained according to the pollutant residual index, the leakage of waste acid recovery, the hazard of the reactants and the damage degree of the recovery material until the training converges, and a recovery risk analysis model is output; the recovery risk analysis model is converged by minimizing the objective function, and the expression is as follows:

[0091]

[0092] Among them, min(w,b) represents the objective function to be minimized to obtain the best classification decision boundary, w is the weight vector, which represents the weight parameter of the classification model and is used to map the input feature vector to the classification decision parameter; b is the bias term, which represents the intercept parameter of the classification model and is used to adjust the position of the classification boundary, C is the regularization parameter, which is used to control the penalty for classification errors, m is the number of samples after convolution, and y is the weight parameter of the classification model. i is the label of the sample’s classification category, z i is the feature vector of the i-th sample, max(0,1-y i (w T z i +b) is the loss function. For each sample i, if the predicted value of the classifier is greater than or equal to 1, the loss is 0, otherwise the loss is 1-y i (w T z i +b).

[0093] Furthermore, the system is also used to implement the following functions:

[0094] The mixed recovery treatment module includes at least one mixing reactor and a multi-stage separation device, and the multi-stage separation device is used to perform multi-stage separation on the waste acid after the mixed recovery treatment; wherein, the multi-stage separation device includes a filter, a sedimentation tank and an evaporator, the filter is used to preliminarily remove solid impurities, the sedimentation tank is used to precipitate and separate heavy metal ions, and the evaporator is used to concentrate and recover the remaining acid liquid.

[0095] Furthermore, the system is also used to implement the following functions:

[0096] The single recovery processing module includes at least one single reactor and an acid regeneration system; the single reactor is used to adapt the waste acid treatment type according to the waste acid sample data set in the single recovery processing node, and perform waste acid recovery processing according to the adaptation result; the acid regeneration system is used to restore the acid after the single recovery treatment to the initial acid concentration, and circulate it into the titanium dioxide production process.

[0097] Furthermore, the system is also used to implement the following functions:

[0098] The time sequence of the mixed recovery processing nodes is obtained; taking the preset processing risk index as the target, the mixed recovery processing nodes are mixed clustered according to the time sequence, and the mixed clustering result is output; the mixed recovery processing module recycles the waste acid of the mixed recovery processing nodes according to the mixed clustering result.

[0099] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0101] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent recovery and treatment method for waste acid in titanium dioxide production, characterized in that: The method comprises: Acquire multiple process nodes of a titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes; A plurality of pipeline systems are provided according to the plurality of process nodes, wherein each pipeline system is used to collect waste acid of a corresponding process node, and a diverter valve is provided at the outlet of each pipeline, and a path of the waste acid in the pipeline is changed according to the diverter valve; Setting a collaborative recycling processing module, the collaborative recycling processing module comprising a mixed recycling processing module and a single recycling processing module; Inputting the plurality of waste acid sample data sets into a recycling risk analysis model, and dividing the plurality of process nodes according to the recycling risk analysis model to identify them as mixed recycling processing nodes and single recycling processing nodes; The mixed recovery processing module recovers the waste acid of the mixed recovery processing node, and the single recovery processing module recovers the waste acid of the single recovery processing node.

2. The method according to claim 1, characterized in that According to the plurality of process nodes, a plurality of pipeline systems are set, and the method include: Wherein, the pipeline inlet of each pipeline system is connected to the reactor outlet of the corresponding process node, and the pipeline outlet includes a first outlet and a second outlet, the first outlet is connected to the reactor inlet of the mixed recovery processing module, and the second outlet is connected to the reactor inlet of the single recovery processing module; The path of the waste acid in the pipeline is changed according to the diverter valve, and the waste acid flows into the mixed reactor of the mixed recovery processing module or the single reactor of the single recovery processing module.

3. The method according to claim 1, characterized in that Inputting the plurality of spent acid sample data sets into a recovery risk analysis model, the method comprising: Acquire the plurality of waste acid sample data sets, wherein each waste acid sample data set includes component data, acid concentration, metal ion content, and organic matter content; performing high-dimensional risk feature mapping analysis on each of the multiple waste acid sample data sets to obtain multiple treatment risk indicators; According to the mixed recovery device of the mixed recovery processing module, a preset processing risk is set; The multiple processing risk indicators are judged based on the preset processing risk indicator, and the process nodes greater than or equal to the preset processing risk indicator are identified as single recycling processing nodes, and the process nodes less than the preset processing risk indicator are identified as mixed recycling processing nodes.

4. The method according to claim 3, characterized in that Performing a high-dimensional risk feature mapping analysis on each of the multiple waste acid sample data sets, the method comprising: Performing high-dimensional risk feature mapping on each of the multiple waste acid sample data sets through an RBF kernel function to obtain pollutant residue indicators, waste acid recovery leakage, reactant hazard, and recovery material damage degree; The expression of the RBF kernel function is as follows: Among them, K(x i , x j ) is the output value of the kernel function, sample x i and sample x j The inner product in high-dimensional space is used to measure the similarity between samples, x i is the feature vector of the i-th waste acid sample data set, x j is the feature vector of the jth waste acid sample data set, ‖|x i -x j || 2 For x i and sample x j The square of the Euclidean distance between them represents the distance between two samples in the feature space. σ is the width parameter of the kernel function, which controls the decay speed of the similarity between samples.

5. The method according to claim 4, characterized in that Get the initialized support vector machine; The initialized support vector machine is trained according to the residual index of pollutants, leakage of waste acid recovery, dangerousness of reactants and damage degree of recovery materials until the training converges, and a recovery risk analysis model is output; The recovery risk analysis model is converged by minimizing the objective function, which is expressed as follows: Among them, min(w, b) represents the objective function to be minimized to obtain the best classification decision boundary, w is the weight vector, which represents the weight parameter of the classification model and is used to map the input feature vector to the parameter of the classification decision; b is the bias term, which represents the intercept parameter of the classification model and is used to adjust the position of the classification boundary. C is the regularization parameter, which is used to control the penalty intensity of classification errors. m is the number of samples after convolution. i is the label of the sample’s classification category, z i is the feature vector of the i-th sample, max(0, 1-y i (w T z i +b) is the loss function. For each sample i, if the predicted value of the classifier is greater than or equal to 1, the loss is 0, otherwise the loss is 1-y i (w T z i +b).

6. The method according to claim 1, characterized in that The mixed recovery treatment module includes at least one mixing reactor and a multi-stage separation device, and the multi-stage separation device is used to perform multi-stage separation on the waste acid after the mixed recovery treatment; The multi-stage separation device comprises a filter, a sedimentation tank and an evaporator. The filter is used for preliminary removal of solid impurities, the sedimentation tank is used for precipitation and separation of heavy metal ions, and the evaporator is used for concentrating and recovering the remaining acid solution.

7. The method according to claim 1, characterized in that The single recovery processing module includes at least one single reactor and an acid regeneration system; The single reactor is used to adapt the waste acid treatment type according to the waste acid sample data set in the single recovery and treatment node, and perform waste acid recovery and treatment according to the adaptation result; The acid solution regeneration system is used to restore the acid solution after single recovery treatment to the initial acid concentration, and circulate it into the titanium dioxide production process.

8. The method according to claim 3, characterized in that After identifying the process node with a risk index less than the preset processing risk index as a mixed recovery processing node, the method further includes: Obtaining the time sequence of the mixed recycling processing nodes; Taking the preset processing risk index as a target, performing hybrid clustering on the hybrid recycling processing nodes in the time sequence, and outputting a hybrid clustering result; The mixed recovery processing module recovers the waste acid of the mixed recovery processing node according to the mixed clustering result.

9. Intelligent recovery and treatment system for waste acid in titanium dioxide production, characterized in that: The system comprises: A process node acquisition unit, the process node acquisition unit is used to acquire multiple process nodes of the titanium dioxide production process, and collect multiple waste acid sample data sets corresponding to the multiple process nodes; A pipeline system setting unit, the pipeline system setting unit is used to set a plurality of pipeline systems according to the plurality of process nodes, wherein each pipeline system is used to collect waste acid of a corresponding process node, and a diverter valve is provided at the outlet of each pipeline, and a path of the waste acid in the pipeline is changed according to the diverter valve; A processing module setting unit, the processing module setting unit is used to set a collaborative recycling processing module, the collaborative recycling processing module includes a mixed recycling processing module and a single recycling processing module; A node division unit, wherein the node division unit inputs the plurality of waste acid sample data sets into a recovery risk analysis model, and divides the plurality of process nodes according to the recovery risk analysis model to identify them as mixed recovery processing nodes and single recovery processing nodes; A recycling processing unit, wherein the recycling processing unit is used for the mixed recycling processing module to recycle the waste acid of the mixed recycling processing node, and the single recycling processing module to recycle the waste acid of the single recycling processing node.

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