Sludge purification and recovery control method, system and equipment
Through the correlation analysis of multivariate detection and multi-order control variable chains, combined with the recovery quality prediction model and variation optimization mechanism, the problems of unstable efficiency and quality in the sludge recovery process were solved, and the sludge recovery efficiency and quality were improved.
Patent Information
- Application Number
- CN202510897743.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-01
- Publication Date
- 2025-09-23
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The sludge recovery process in the existing technology lacks targeted multi-stage control, resulting in unstable recovery efficiency and quality.
Sludge is detected through multivariate detection indicators, a multi-order recycling control variable chain is constructed, correlation analysis and collaborative analysis are performed, and the recycling quality prediction model and variation optimization mechanism are combined to achieve multi-degree-of-freedom joint optimization.
The efficiency and quality stability of sludge recovery are improved, and the problem of inconsistent efficiency and quality in the sludge recovery process is solved.
Smart Images
Figure CN120686707A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of sludge treatment, and in particular to a sludge purification and recovery control method, system and equipment. Background Art
[0002] Sludge, a key wastewater treatment product in water pollution control, has a direct impact on the removal of water pollutants and resource reuse. Traditional sludge treatment typically relies on single or limited process parameter adjustments, lacking multi-stage control of sludge characteristics and dynamic optimization of recovery quality. This results in inadequate guarantees for recovery efficiency and quality consistency. Summary of the Invention
[0003] The present application provides a sludge purification and recovery control method, system and equipment, which are used to solve the technical problem that the sludge recovery process in water pollution control in the existing technology lacks targeted multi-stage control, resulting in low recovery efficiency and quality.
[0004] In view of the above problems, the present application provides a sludge purification and recovery control method, system and equipment.
[0005] In a first aspect of the present application, a sludge purification and recovery control method is provided, the method comprising: The sludge to be treated is tested according to multivariate detection indicators to obtain sludge detection data; control variables are configured according to the sludge purification and recovery process to construct a multi-order recovery control variable chain; the sludge detection data are correlated and analyzed according to the multi-order recovery control variable chain to obtain each-order recovery correlation detection sequence; multi-order recovery control collaborative analysis is performed on the multi-order recovery control variable chain according to each-order recovery correlation detection sequence to establish a first sludge recovery control group; recovery quality optimization is performed on the first sludge recovery control group according to a recovery quality prediction model to obtain a second sludge recovery control group; a recovery variation optimization mechanism is introduced to expand the second sludge recovery control group to obtain a third sludge recovery control group, and multi-degree-of-freedom joint optimization is performed on the third sludge recovery control group to obtain a sludge recovery control optimization strategy.
[0006] The second aspect of the present application provides a sludge purification and recovery control system, the system comprising: A detection module is used to detect the sludge to be treated according to multiple detection indicators to obtain sludge detection data; a control variable configuration module is used to configure control variables according to the sludge purification and recovery process to construct a multi-order recovery control variable chain; an association analysis module is used to perform association analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain each-order recovery association detection sequence; a collaborative analysis module is used to perform multi-order recovery control collaborative analysis on the multi-order recovery control variable chain according to the each-order recovery association detection sequence to establish a first sludge recovery control group; an optimization module is used to perform recovery quality optimization on the first sludge recovery control group according to a recovery quality prediction model to obtain a second sludge recovery control group; an optimization strategy determination module is used to introduce a recovery variation optimization mechanism to expand the second sludge recovery control group to obtain a third sludge recovery control group, and perform multi-degree-of-freedom joint optimization on the third sludge recovery control group to obtain a sludge recovery control optimization strategy.
[0007] The third aspect of the present application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement a sludge purification and recovery control method provided in the present application.
[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application detects the treated sludge according to the multivariate detection index to obtain sludge detection data; configures the control variables according to the sludge purification and recovery process to construct a multi-order recovery control variable chain; performs correlation analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain each order recovery correlation detection sequence; performs multi-order recovery control collaborative analysis on the multi-order recovery control variable chain according to the each order recovery correlation detection sequence to establish a first group of sludge recovery control; performs recovery quality optimization on the first group of sludge recovery control according to the recovery quality prediction model to obtain a second group of sludge recovery control; introduces a recovery variation optimization mechanism to expand the second group of sludge recovery control to obtain a third group of sludge recovery control, and performs multi-degree-of-freedom joint optimization on the third group of sludge recovery control to obtain a sludge recovery control optimization strategy. The present invention solves the technical problem that the sludge recovery process in water pollution control in the prior art lacks targeted multi-stage control, resulting in low recovery efficiency and quality. Through multi-order control and joint optimization mechanism, the technical effect of improving sludge recovery efficiency and quality stability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] 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.
[0010] Figure 1 A schematic flow chart of a sludge purification and recovery control method provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a sludge purification and recovery control system provided in an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.
[0011] Explanation of the accompanying drawings: bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305, detection module 11, control variable configuration module 12, association analysis module 13, collaborative analysis module 14, optimization module 15, optimization strategy determination module 16. DETAILED DESCRIPTION
[0012] This application provides a sludge purification and recovery control method, system and equipment to solve the technical problem of the lack of efficient control and quality optimization means in the sludge recovery process in water pollution control in the existing technology. Through multi-stage control and joint optimization mechanism, the technical effect of improving sludge recovery efficiency and quality stability is achieved.
[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0014] It should be noted that any variations of the terms "include" and "have" 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 clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.
[0015] Example 1, as Figure 1 As shown, the present application provides a sludge purification and recovery control method, the method comprising: Step S100: Detecting the sludge to be treated according to the multivariate detection indexes to obtain sludge detection data.
[0016] Furthermore, the method provided in the application embodiment also includes: The multivariate detection indicators include sludge type, sludge rheology, sludge solid concentration, unit volume mass, particle size characteristics, floc structure characteristics, sludge moisture content, pH value, nutrients, heavy metals, organic pollutants and microorganisms.
[0017] In an embodiment of the present application, in order to purify and recycle the treated sludge, the treated sludge is first tested based on multiple detection indicators. Specifically, the treated sludge is first sampled through a sampling unit, and then the sludge type and unit volume mass are obtained using a density meter and a weight method. The sludge rheology is measured using a rotational rheometer, the sludge solid concentration is measured by gravity sedimentation and vacuum filtration, the particle size characteristics are obtained using a laser particle size analyzer, the floc structure characteristics are detected by optical microscopy, the sludge moisture content is calculated using a drying method, the pH value is measured using a pH meter, heavy metals are analyzed using an atomic absorption spectrometer (AAS) or an inductively coupled plasma mass spectrometer (ICP-MS), organic pollutants are detected by a gas chromatography-mass spectrometry (GC-MS), nutrients are determined using a total nitrogen and total phosphorus analyzer, and microorganisms are detected by combining a microscope counting method or molecular probe technology. Through the above detection methods and steps, sludge detection data is obtained.
[0018] Step S200: configuring control variables according to the sludge purification and recovery process, and constructing a multi-stage recovery control variable chain.
[0019] In an embodiment of the present application, when configuring control variables according to the sludge purification and recovery process, the sludge purification and recovery process is first refined into a chemical conditioning process, a pollution degradation process and a dissolution recovery process through a process decomposition step, and control variables are configured for each process stage respectively, and a conditioning control variable chain, a degradation control variable chain and a dissolution control variable chain are established in turn. Finally, these three control variable chains are integrated to form a multi-stage recovery control variable chain.
[0020] Furthermore, in the method provided in the embodiment of the application, control variables are configured according to the sludge purification and recovery process to construct a multi-stage recovery control variable chain, which also includes: The sludge purification and recovery process is disassembled to obtain a chemical conditioning process, a pollution degradation process and a dissolution recovery process; control variables are configured according to the chemical conditioning process to establish a conditioning control variable chain; control variables are configured according to the pollution degradation process to establish a degradation control variable chain; control variables are configured according to the dissolution recovery process to establish a dissolution control variable chain; the conditioning control variable chain, the degradation control variable chain and the dissolution control variable chain are added to the multi-stage recovery control variable chain.
[0021] In the embodiment of the present application, the sludge purification and recovery process is first disassembled through the process disassembly steps. Combined with the existing sludge treatment design method, the process flow is divided into three stages according to the treatment order: early stage, middle stage and late stage. The early stage is a chemical conditioning process, the middle stage is a pollution degradation process, and the late stage is a dissolution recovery process.
[0022] Next, for the disassembled chemical conditioning process, the conditioning recovery scheme search step was carried out through methods such as literature retrieval and the organization of typical reagent experimental schemes. This resulted in a set of conditioning recovery schemes for different sludge types. Parameter extraction methods were then used to identify the type of reagent, dosage, agitation rate, conditioning time, and other factors involved in the schemes, generating the first set of conditioning control variables. Trigger degree calculation was then used to statistically analyze the frequency and impact of each variable across multiple schemes. The second set of conditioning control variables was then obtained by screening based on a set trigger degree threshold. A chain variable sorting method was then used to organize the order and dependencies between the variables, generating a chain of conditioning control variables.
[0023] For the disassembled pollution degradation process and dissolution recovery process, the same control variable configuration ideas and steps as the chemical conditioning process are adopted, that is, first obtain the degradation recovery plan set or dissolution recovery plan set through the plan retrieval step, then perform variable identification, trigger degree calculation and screening, and finally organize the order and dependency relationship between variables through chain combing to generate degradation control variable chains and dissolution control variable chains respectively. Specifically, for the disassembled pollution degradation process, by querying the biochemical treatment standards and existing biochemical degradation experimental plans, the degradation recovery plan retrieval step is executed to obtain the degradation recovery plan set suitable for different organic loads and microbial conditions, from which parameters such as dissolved oxygen, temperature, reaction time, and microbial inoculum are extracted to form the first set of degradation control variables. The same trigger degree calculation and screening method is used to obtain the second set of degradation control variables, and the degradation control variable chain is generated through chain sorting. For the disassembled dissolution recovery process, referring to the metal dissolution experiment and solvent leaching plan, the dissolution recovery plan retrieval step is executed to sort out the solvent types, concentrations, temperatures and dissolution times for the recovery of different heavy metals and nutrients, forming the first set of dissolution control variables. The second set of dissolution control variables is obtained through trigger degree calculation and screening, and then the dissolution control variable chain is generated through chain sorting.
[0024] Finally, the conditioning control variable chain, degradation control variable chain and dissolution control variable chain are combined in process order to form a complete multi-stage recovery control variable chain.
[0025] Furthermore, in the method provided in the embodiment of the application, control variables are configured according to the chemical conditioning process to establish a conditioning control variable chain, and the method further includes: Based on the chemical conditioning process, sludge recovery schemes are retrieved to obtain a set of conditioning recovery schemes; variables are identified based on the set of conditioning recovery schemes to obtain a first set of conditioning control variables; the triggering degree of each conditioning control variable in the first set of conditioning control variables is calculated based on the set of conditioning recovery schemes to obtain the triggering degree of each variable; based on the triggering degree of each variable, the first set of conditioning control variables is selected according to a predetermined triggering degree to obtain a second set of conditioning control variables; chain combing is performed based on the second set of conditioning control variables to generate the conditioning control variable chain.
[0026] In the embodiments of the present application, firstly, based on the chemical conditioning process, through the conditioning scheme retrieval method, the chemical conditioning treatment schemes applicable to different sludge types and sources are sorted out by adopting steps such as keyword retrieval, patent database comparison and industry application example collection, and the core parameters such as the types of conditioning agents involved, addition sequence, addition concentration, stirring rate and conditioning time are summarized to form a conditioning recovery scheme set.
[0027] After obtaining the set of conditioning and recovery plans, the variable identification method is used to analyze the content of each specific plan in the conditioning and recovery plan set, extract the adjustable and controllable parameters involved in the conditioning operation, and deduplicate these parameters to form the first set of conditioning control variables. This variable set includes key variables such as the type of conditioning agent, the amount of conditioning agent added, the stirring rate, the stirring time, the conditioning temperature, and the conditioning pH value.
[0028] Then, for the first set of conditioning control variables, the variable trigger degree calculation method is used. Combined with the entire conditioning recovery plan set, each conditioning control variable is traversed and counted, and its frequency of occurrence in all plans is calculated. The ratio of the number of occurrences to the total number of plans is converted into a percentage to obtain the variable trigger degree corresponding to each conditioning control variable.
[0029] Then, based on the calculated variable trigger degree, a predetermined trigger degree threshold (such as 50%) is set through the trigger degree threshold screening method, and the conditioning control variables with a trigger degree greater than or equal to the predetermined threshold are screened out from the first set of conditioning control variables, and the second set of conditioning control variables is formed after aggregation.
[0030] Finally, based on the second set of conditioning control variables, the chain variable combing method is adopted. Combined with the logical order of variables in the conditioning operation and the dependency relationship between parameters, the variables are sorted and associated according to process steps such as conditioning agent selection, agent addition order, dosage setting, stirring rate setting and conditioning time setting, and finally a complete and clearly ordered conditioning control variable chain is generated.
[0031] Step S300: performing correlation analysis on the sludge detection data according to the multi-stage recovery control variable chain to obtain a correlation detection sequence for each stage of recovery.
[0032] In an embodiment of the present application, when the sludge detection data is correlated and analyzed based on the multi-order recovery control variable chain, the stage variable matching method is first adopted, and the constructed multi-order recovery control variable chain is used as the basis for analysis, and it is disassembled into three parts: conditioning control variable chain, degradation control variable chain and dissolution control variable chain.
[0033] For the conditioning control variable chain, the detection parameter screening method is used to compare the variables such as the type of conditioning agent, conditioning agent dosage, stirring rate, conditioning time in the conditioning control variable chain with the sludge type, sludge solid concentration, floc structure characteristics, sludge moisture content, pH value and other parameters in the sludge detection data, match and extract data item by item, and organize the matched related detection data in the order of variables to generate a conditioning related detection sequence.
[0034] For the degradation control variable chain, the detection parameter screening method is also used to screen out detection information directly related to variables such as dissolved oxygen concentration, temperature, reaction time, and microbial load from the sludge detection data, mainly including organic pollutants, microorganisms, and nutrient element data. After completing the correspondence, they are combined to form a degradation-related detection sequence.
[0035] For the dissolution control variable chain, the detection parameter screening method is continued to match the detection parameters such as heavy metals, nutrients, particle size characteristics in the sludge detection data with dissolution variables such as solvent type, solvent concentration, dissolution temperature, and dissolution time. The corresponding data are extracted and summarized into a dissolution-related detection sequence.
[0036] Through the above matching and screening steps, according to the item-by-item correspondence between the control variables and the test data at each stage, the recovery-related detection sequences of each order are obtained, including the conditioning-related detection sequence, the degradation-related detection sequence and the dissolution-related detection sequence.
[0037] Step S400: performing multi-stage recovery control collaborative analysis on the multi-stage recovery control variable chain according to the respective recovery association detection sequences, and establishing a first sludge recovery control group.
[0038] In an embodiment of the present application, when performing a multi-stage recycling control collaborative analysis on a multi-stage recycling control variable chain according to each stage recycling associated detection sequence, the obtained conditioning associated detection sequence is first matched and parameter decisions are made with the conditioning control variable chain item by item to determine a conditioning control scheme set suitable for the current sludge state. Subsequently, the degradation associated detection sequence and the degradation control variable chain are analyzed accordingly, and control parameter decisions are made to obtain a degradation control scheme set. At the same time, the dissolution associated detection sequence and the dissolution control variable chain are parameter matched and analyzed to generate a dissolution control scheme set. After obtaining the control scheme set for the three stages, a multi-stage control scheme combination method is adopted to combine and cross-match the conditioning control scheme set, the degradation control scheme set and the dissolution control scheme set according to the process sequence and the variable dependency, and finally a multi-stage control combination scheme is generated, which is summarized to form the first group of sludge recovery control.
[0039] Furthermore, in the method provided in the embodiment of the application, multi-stage recovery control collaborative analysis is performed on the multi-stage recovery control variable chain according to the respective recovery association detection sequences to establish a first sludge recovery control group, further comprising: The recovery-related detection sequences of each order include a conditioning-related detection sequence, a degradation-related detection sequence and a dissolution-related detection sequence; control parameter decisions are made on the conditioning control variable chain according to the conditioning-related detection sequence to obtain a conditioning control scheme set; control parameter decisions are made on the degradation control variable chain according to the degradation-related detection sequence to obtain a degradation control scheme set; control parameter decisions are made on the dissolution control variable chain according to the dissolution-related detection sequence to obtain a dissolution control scheme set; multi-order control schemes are combined according to the conditioning control scheme set, the degradation control scheme set and the dissolution control scheme set to generate the first group of sludge recovery control.
[0040] In the embodiment of the present application, each stage of recovery-related detection sequence includes a conditioning-related detection sequence, a degradation-related detection sequence, and a dissolution-related detection sequence.
[0041] When making control parameter decisions for the conditioning control variable chain based on the conditioning association detection sequence, a conditioning parameter matching method is used to match the parameters of the conditioning control variable chain and the conditioning association detection sequence item by item. Specifically, the sludge type, sludge solid concentration, floc structure characteristics, sludge moisture content, and pH value in the sludge detection data are selected as key detection parameters for the conditioning stage. Combined with the control variables such as the type of conditioning agent, conditioning agent dosage, stirring rate, and conditioning time in the established conditioning control variable chain, a control parameter solution method is used to automatically generate several groups of control parameter configurations that are feasible and stable under the current sludge state through table matching, rule determination, or rule base (such as process recommendation rules). Multiple executable conditioning control schemes are obtained, and these schemes are combined into a conditioning control scheme set.
[0042] The degradation control variable chain is then processed using a variable interval matching method. Based on parameters such as organic pollutant concentration, microbial count, and nutrient levels in the degradation-related test sequence, a pre-set variable control interval table is matched. By comparing the test values within these intervals, the corresponding available combinations of control variables such as dissolved oxygen concentration, reaction temperature, reaction time, and microbial load are selected and summarized into multiple groups of degradation control scenarios, forming a degradation control scenario set.
[0043] Next, the dissolution control variable chain is processed. Using a conditional screening method, based on test data such as heavy metal concentration, particle size characteristics, and nutrient concentrations from the dissolution-related test sequence, screening conditions for solvent type, solvent concentration, dissolution temperature, and dissolution time are set. By eliminating combinations that don't match the current test data from the established library of control variable candidates and retaining all control configurations that meet the screening conditions, multiple dissolution control plans are formed and summarized into a dissolution control plan set.
[0044] After completing the generation of the control scheme sets for the three stages, the Cartesian combination method of the schemes is adopted. The conditioning control scheme set, degradation control scheme set and dissolution control scheme set are used as inputs, and they are arranged and combined one by one according to the process sequence to form all possible multi-stage control scheme combinations.
[0045] A parameter consistency verification method was then used to verify the cross-stage compatibility of the above-mentioned combination schemes. This verification process specifically involves first extracting key variable parameters in each combination scheme, such as conditioning pH, degradation temperature, reaction time, and final sludge state parameters (moisture content, solids content). Then, based on the preset parameter transfer logic table, it is determined whether there are logical conflicts or range overlap errors between variables in adjacent stages. For example, if the pH adjustment range in the conditioning control scheme is 5.0–6.0, and the degradation control scheme requires an initial pH value of no less than 6.5, then the combination does not meet the transfer conditions and should be eliminated. The above verification process is completed using a rule matching method, and all combination schemes that do not meet the conditions will be eliminated from the result set.
[0046] Finally, all the multi-stage control combination schemes that have passed the parameter consistency check are summarized to form the first group of sludge recovery control that includes multiple complete and executable control configurations.
[0047] Step S500: optimizing the recovery quality of the first sludge recovery control group according to the recovery quality prediction model to obtain the second sludge recovery control group.
[0048] In an embodiment of the present application, when optimizing the recovery quality of the first group of sludge recovery control according to the recovery quality prediction model, all control schemes in the first group of sludge recovery control are first traversed, and the qth sludge recovery control scheme is extracted one by one, and the scheme and the corresponding sludge detection data are input into the recovery quality prediction model with Q sludge recovery quality prediction layers to obtain Q recovery quality prediction coefficients. The recovery quality prediction model is pre-constructed. Subsequently, the mean of the Q prediction coefficients is calculated to generate the sludge recovery quality coefficient of the scheme, and compared with the preset sludge recovery quality threshold. If the quality coefficient is greater than or equal to the threshold, the scheme is included in the second group of sludge recovery control. Through the above-mentioned traversal and screening process, the recovery quality optimization of the first group of sludge recovery control is finally completed, and the second group of sludge recovery control is obtained.
[0049] Furthermore, in the method provided in the embodiment of the application, the recovery quality of the first group of sludge recovery control is optimized according to the recovery quality prediction model to obtain the second group of sludge recovery control, and the method further includes: According to the first group of sludge recovery control, the qth sludge recovery control scheme is extracted, where q is a positive integer; the qth sludge recovery control scheme and the sludge detection data are input into the recovery quality prediction model to obtain Q recovery quality prediction coefficients, where the recovery quality prediction model includes Q sludge recovery quality prediction layers, where Q is a positive integer greater than 1; the mean of the Q recovery quality prediction coefficients is calculated to generate the qth sludge recovery quality coefficient, and it is determined whether the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold; if the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold, the qth sludge recovery control scheme is added to the second group of sludge recovery control.
[0050] In the embodiment of the present application, a random scheme extraction method is first adopted to randomly extract a control scheme from the first group of sludge recovery control, which is recorded as the qth scheme of sludge recovery control, where q is a positive integer.
[0051] Subsequently, the qth sludge recovery control scheme and the corresponding sludge detection data are combined and input into the recovery quality prediction model for analysis. The recovery quality prediction model consists of Q independent sludge recovery quality prediction layers, each of which is an independent recovery quality prediction algorithm, such as linear regression algorithm, support vector regression, random forest regression, decision tree regression or neural network regression, etc. Q is a positive integer greater than 1. Each sludge recovery quality prediction layer is trained using exactly the same training data set during the training phase. The training input is a combination of sludge recovery control schemes and sludge detection data constructed in historical samples. The training output is the recovery quality prediction coefficient annotated by technical experts to measure the recovery performance of the scheme in actual operation. After the training is completed, all prediction layers are combined to form the recovery quality prediction model.
[0052] In the model application stage, Q sludge recovery quality prediction layers analyze and process the qth sludge recovery control scheme and the corresponding sludge detection data respectively, and output a recovery quality prediction coefficient respectively, thereby obtaining Q recovery quality prediction coefficients.
[0053] Then, the mean calculation method is used to numerically average these Q coefficients to obtain the qth sludge recovery quality coefficient corresponding to the qth scheme of the current sludge recovery control.
[0054] Finally, the qth sludge recovery quality coefficient is compared with the sludge recovery quality threshold value preset by technical experts. If the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold value, the qth sludge recovery control scheme is added to the second sludge recovery control group.
[0055] Step S600: introducing a recycling variation optimization mechanism to expand the second sludge recycling control group to obtain a third sludge recycling control group, and performing multi-degree-of-freedom joint optimization on the third sludge recycling control group to obtain a sludge recycling control optimization strategy.
[0056] In an embodiment of the present application, a recycling variation optimization mechanism is first introduced to expand the second group of sludge recycling control to form a third group of sludge recycling control. This process constructs a first-round variation value distribution by performing a sludge recycling quality ratio calculation on each sludge recycling control scheme in the second group of sludge recycling control, and implements parameter perturbation based on this to generate the first-round recycling variation space. Subsequently, the space is quality-optimized in combination with the recycling quality prediction model to screen out the first-round variation optimization space and use it to expand the second group of sludge recycling control. This variation optimization process is executed progressively for multiple rounds until the preset number of variation optimization rounds is reached, and finally a third group of sludge recycling control containing diversified improvement schemes is generated.
[0057] Next, a multi-degree-of-freedom joint optimization operation was performed on the third group of sludge recovery control. This operation first traversed and analyzed the third group of solutions based on the predetermined sludge recovery efficiency, selecting solutions that met the efficiency requirements to form the fourth group of sludge recovery control. A second round of screening was then performed based on the predetermined sludge recovery cost, extracting solutions that met the cost constraints to form the fifth group of sludge recovery control. Finally, through the intersection identification of the fourth and fifth groups, an optimization strategy for sludge recovery control that balances efficiency and cost requirements was obtained.
[0058] Furthermore, in the method provided in the embodiment of the application, the recovery variation optimization mechanism further includes: The sludge recovery quality ratio is calculated according to each sludge recovery control scheme in the second sludge recovery control group to obtain the first round of variation value distribution; the second sludge recovery control group is mutated according to the first round of variation value distribution to obtain the first round of recovery variation space; the first round of recovery variation space is optimized for recovery quality according to the recovery quality prediction model to obtain the first round of variation optimization space; the second sludge recovery control group is expanded according to the first round of variation optimization space, and the recovery variation optimization is continued according to the first round of variation optimization space until the number of variation optimization rounds reaches the predetermined number of variation optimization rounds, and the third sludge recovery control group is generated.
[0059] In this embodiment of the present application, the corresponding sludge recovery quality coefficient is first extracted for each sludge recovery control scheme in the second sludge recovery control group. Next, a weighted proportion calculation method is used to accumulate the sludge recovery quality coefficients of all schemes to obtain the total quality value of the group. The sludge recovery quality coefficient of each scheme is then divided by the total quality value to obtain the corresponding variation value, which reflects the variation proportion weight that the scheme should occupy in the current group. The variation value of all schemes is combined to form the first round of variation value distribution.
[0060] Next, based on the first-round variation value distribution and a predetermined total number of variations, N (e.g., set to 100), the variation value of each sludge recovery control scheme is multiplied by N to obtain the number of variations corresponding to that scheme. For each scheme, a parameter perturbation operation is performed according to the corresponding number of variations. This involves generating new parameter combinations based on the original scheme's control parameters (e.g., chemical conditioning agent dosage, degradation time, dissolution pH, etc.) with a preset perturbation amplitude (e.g., ±10%). This results in a set of schemes with the same structure as the original scheme but slightly different parameters. The total set of these combinations constitutes the first-round recovery variation space.
[0061] Each new solution within the first round of recycling variation space is then paired with sludge testing data and input into the trained recycling quality prediction model. This step is identical to the recycling quality prediction and screening process for the first group of sludge recycling control. Specifically, each variation solution is paired with sludge testing data and input. The recovery quality prediction coefficient is obtained through each prediction layer in the recycling quality prediction model. The mean of these coefficients is calculated to form the sludge recovery quality coefficient, which is then compared with the sludge recovery quality threshold to select variations that meet the quality requirements. These variations constitute the first round of variation optimization space.
[0062] Finally, the solution union expansion method is used to merge the first round of mutation optimization space into the original second sludge recovery control group, creating a new sludge recovery control group. The entire process of "quality proportion calculation → variant generation → quality prediction → solution screening → population expansion" is then repeated based on this updated group until the number of mutation optimization iterations reaches the predetermined number of mutation optimization rounds R (for example, 5 rounds). Ultimately, the set of all solutions retained after R rounds of iterative expansion and quality screening constitutes the third sludge recovery control group.
[0063] Furthermore, in the method provided in the embodiment of the application, multi-degree-of-freedom joint optimization is performed on the third group of sludge recovery control to obtain a sludge recovery control optimization strategy, which also includes: According to the predetermined sludge recovery efficiency, the third group of sludge recovery control is traversed and optimized to establish a fourth group of sludge recovery control that is greater than or equal to the predetermined sludge recovery efficiency; according to the predetermined sludge recovery cost, the third group of sludge recovery control is traversed and optimized to establish a fifth group of sludge recovery control that is less than or equal to the predetermined sludge recovery cost; according to the intersection identification of the fourth group of sludge recovery control and the fifth group of sludge recovery control, the sludge recovery control optimization strategy is generated.
[0064] In an embodiment of the present application, an efficiency prediction model is first used to evaluate the efficiency of each sludge recovery control scheme in the third group of sludge recovery control. The efficiency prediction model is a feedforward neural network model trained based on previous experimental data. The input is the process control parameters corresponding to the sludge recovery control scheme (including conditioning control parameters, degradation control parameters, and dissolution control parameters), such as pH regulator dosage, degradation agent type and dosage, heating temperature and duration, etc. The output is the sludge recovery efficiency prediction value corresponding to the scheme. By inputting each control scheme into the efficiency prediction model, the corresponding recovery efficiency prediction result is traversed and generated, and compared with a predetermined sludge recovery efficiency threshold, a set of schemes with efficiency prediction values greater than or equal to the threshold are screened out to form the fourth group of sludge recovery control.
[0065] A cost prediction model was then used to evaluate the cost of each sludge recovery control scheme in the third sludge recovery control cluster. This model, also a trained neural network, uses the same inputs as the efficiency prediction model: a scheme feature vector composed of control parameters. Its output is the predicted sludge recovery cost for each scheme. By sequentially inputting each control scheme into the cost prediction model, the cost prediction results for all schemes are calculated and compared with a preset sludge recovery cost threshold. The set of schemes with cost predictions less than or equal to the threshold is selected to form the fifth sludge recovery control cluster.
[0066] Finally, the set cross-identification method is used to identify the intersection of the fourth and fifth groups of sludge recovery control, extracting a set of sludge recovery control solutions that meet both efficiency and cost requirements. This intersection set is the sludge recovery control optimization strategy.
[0067] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application detects the treated sludge according to the multivariate detection index to obtain sludge detection data; configures the control variables according to the sludge purification and recovery process to construct a multi-order recovery control variable chain; performs correlation analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain each order recovery correlation detection sequence; performs multi-order recovery control collaborative analysis on the multi-order recovery control variable chain according to the each order recovery correlation detection sequence to establish a first group of sludge recovery control; performs recovery quality optimization on the first group of sludge recovery control according to the recovery quality prediction model to obtain a second group of sludge recovery control; introduces a recovery variation optimization mechanism to expand the second group of sludge recovery control to obtain a third group of sludge recovery control, and performs multi-degree-of-freedom joint optimization on the third group of sludge recovery control to obtain a sludge recovery control optimization strategy. The present invention solves the technical problem that the sludge recovery process in water pollution control in the prior art lacks targeted multi-stage control, resulting in low recovery efficiency and quality. Through multi-order control and joint optimization mechanism, the technical effect of improving sludge recovery efficiency and quality stability is achieved.
[0068] Example 2, based on the same inventive concept as the sludge purification and recovery control method in the above embodiment, Figure 2 As shown, the present application provides a sludge purification and recovery control system. The system and method embodiments in the present application are based on the same inventive concept. The system includes: The detection module 11 is used to detect the sludge to be treated according to the multivariate detection indicators and obtain the sludge detection data; the control variable configuration module 12 is used to configure the control variables according to the sludge purification and recovery process and construct a multi-order recovery control variable chain; the association analysis module 13 is used to perform association analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain each-order recovery association detection sequence; the collaborative analysis module 14 is used to perform multi-order recovery control collaborative analysis on the multi-order recovery control variable chain according to the each-order recovery association detection sequence to establish a first sludge recovery control group; the optimization module 15 is used to perform recovery quality optimization on the first sludge recovery control group according to the recovery quality prediction model to obtain a second sludge recovery control group; the optimization strategy determination module 16 is used to introduce a recovery variation optimization mechanism to expand the second sludge recovery control group to obtain a third sludge recovery control group, and perform multi-degree-of-freedom joint optimization on the third sludge recovery control group to obtain a sludge recovery control optimization strategy.
[0069] Furthermore, the system is also used to implement the following functions: The sludge purification and recovery process is disassembled to obtain a chemical conditioning process, a pollution degradation process and a dissolution recovery process; control variables are configured according to the chemical conditioning process to establish a conditioning control variable chain; control variables are configured according to the pollution degradation process to establish a degradation control variable chain; control variables are configured according to the dissolution recovery process to establish a dissolution control variable chain; the conditioning control variable chain, the degradation control variable chain and the dissolution control variable chain are added to the multi-stage recovery control variable chain.
[0070] Furthermore, the system is also used to implement the following functions: Based on the chemical conditioning process, sludge recovery schemes are retrieved to obtain a set of conditioning recovery schemes; variables are identified based on the set of conditioning recovery schemes to obtain a first set of conditioning control variables; the triggering degree of each conditioning control variable in the first set of conditioning control variables is calculated based on the set of conditioning recovery schemes to obtain the triggering degree of each variable; based on the triggering degree of each variable, the first set of conditioning control variables is selected according to a predetermined triggering degree to obtain a second set of conditioning control variables; chain combing is performed based on the second set of conditioning control variables to generate the conditioning control variable chain.
[0071] Furthermore, the system is also used to implement the following functions: The recovery-related detection sequences of each order include a conditioning-related detection sequence, a degradation-related detection sequence and a dissolution-related detection sequence; control parameter decisions are made on the conditioning control variable chain according to the conditioning-related detection sequence to obtain a conditioning control scheme set; control parameter decisions are made on the degradation control variable chain according to the degradation-related detection sequence to obtain a degradation control scheme set; control parameter decisions are made on the dissolution control variable chain according to the dissolution-related detection sequence to obtain a dissolution control scheme set; multi-order control schemes are combined according to the conditioning control scheme set, the degradation control scheme set and the dissolution control scheme set to generate the first group of sludge recovery control.
[0072] Furthermore, the system is also used to implement the following functions: According to the first group of sludge recovery control, the qth sludge recovery control scheme is extracted, where q is a positive integer; the qth sludge recovery control scheme and the sludge detection data are input into the recovery quality prediction model to obtain Q recovery quality prediction coefficients, where the recovery quality prediction model includes Q sludge recovery quality prediction layers, where Q is a positive integer greater than 1; the mean of the Q recovery quality prediction coefficients is calculated to generate the qth sludge recovery quality coefficient, and it is determined whether the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold; if the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold, the qth sludge recovery control scheme is added to the second group of sludge recovery control.
[0073] Furthermore, the system is also used to implement the following functions: The sludge recovery quality ratio is calculated according to each sludge recovery control scheme in the second sludge recovery control group to obtain the first round of variation value distribution; the second sludge recovery control group is mutated according to the first round of variation value distribution to obtain the first round of recovery variation space; the first round of recovery variation space is optimized for recovery quality according to the recovery quality prediction model to obtain the first round of variation optimization space; the second sludge recovery control group is expanded according to the first round of variation optimization space, and the recovery variation optimization is continued according to the first round of variation optimization space until the number of variation optimization rounds reaches the predetermined number of variation optimization rounds, and the third sludge recovery control group is generated.
[0074] Furthermore, the system is also used to implement the following functions: According to the predetermined sludge recovery efficiency, the third group of sludge recovery control is traversed and optimized to establish a fourth group of sludge recovery control that is greater than or equal to the predetermined sludge recovery efficiency; according to the predetermined sludge recovery cost, the third group of sludge recovery control is traversed and optimized to establish a fifth group of sludge recovery control that is less than or equal to the predetermined sludge recovery cost; according to the intersection identification of the fourth group of sludge recovery control and the fifth group of sludge recovery control, the sludge recovery control optimization strategy is generated.
[0075] Furthermore, the system is also used to implement the following functions: The multivariate detection indicators include sludge type, sludge rheology, sludge solid concentration, unit volume mass, particle size characteristics, floc structure characteristics, sludge moisture content, pH value, nutrients, heavy metals, organic pollutants and microorganisms.
[0076] Example three. Based on the inventive concept of a sludge purification and recovery control method in the above-mentioned example, the present application also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of any one of the methods described in the above-mentioned example one.
[0077] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In the figure, the bus architecture is represented by bus 300, which can include any number of interconnected buses and bridges. Bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 can be the same component, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 when performing operations.
[0078] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
[0080] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.
Claims
1. A sludge purification and recovery control method, characterized in that: The method comprises: Detect the sludge to be treated according to multiple detection indicators to obtain sludge detection data; Configure control variables according to the sludge purification and recycling process and build a multi-stage recycling control variable chain; Performing correlation analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain a correlation detection sequence for each order of recovery; Perform multi-stage recovery control collaborative analysis on the multi-stage recovery control variable chain according to the respective recovery association detection sequences to establish a first sludge recovery control group; Optimizing the recovery quality of the first sludge recovery control group according to a recovery quality prediction model to obtain a second sludge recovery control group; A recycling variation optimization mechanism is introduced to expand the second group of sludge recycling control to obtain a third group of sludge recycling control, and a multi-degree-of-freedom joint optimization is performed on the third group of sludge recycling control to obtain a sludge recycling control optimization strategy.
2. A sludge purification and recovery control method according to claim 1, characterized in that: Configure control variables according to the sludge purification and recovery process, and build a multi-stage recovery control variable chain, including: Disassembling the sludge purification and recovery process to obtain a chemical conditioning process, a pollution degradation process, and a dissolution recovery process; Configure control variables according to the chemical conditioning process and establish a conditioning control variable chain; Configure control variables according to the pollution degradation process and establish a degradation control variable chain; Configure control variables according to the dissolution recovery process and establish a dissolution control variable chain; The conditioning control variable chain, the degradation control variable chain, and the dissolution control variable chain are added to the multi-stage recovery control variable chain.
3. A sludge purification and recovery control method according to claim 2, characterized in that: According to the chemical conditioning process, control variables are configured to establish a conditioning control variable chain, including: Searching for sludge recovery plans based on the chemical conditioning process to obtain a set of conditioning and recovery plans; Perform variable identification according to the conditioning recovery plan set to obtain a first set of conditioning control variables; Calculating the triggering degree of each conditioning control variable in the first set of conditioning control variables according to the conditioning recovery plan set to obtain the triggering degree of each variable; Based on the triggering degree of each variable, selecting the first set of conditioning control variables according to a predetermined triggering degree to obtain a second set of conditioning control variables; Chain combing is performed based on the second set of conditioning control variables to generate the conditioning control variable chain.
4. A sludge purification and recovery control method according to claim 1, characterized in that: The multi-stage recovery control variable chain is subjected to multi-stage recovery control collaborative analysis according to the respective recovery association detection sequences, and a first sludge recovery control group is established, including: The various stages of recovery-related detection sequences include conditioning-related detection sequences, degradation-related detection sequences, and dissolution-related detection sequences; Performing control parameter decision on the conditioning control variable chain according to the conditioning association detection sequence to obtain a conditioning control scheme set; Performing control parameter decision on the degradation control variable chain according to the degradation association detection sequence to obtain a degradation control solution set; Performing control parameter decision on the dissolution control variable chain according to the dissolution associated detection sequence to obtain a dissolution control solution set; A multi-stage control scheme combination is performed according to the conditioning control scheme set, the degradation control scheme set and the dissolution control scheme set to generate the first sludge recovery control group.
5. A sludge purification and recovery control method according to claim 1, characterized in that: Optimizing the recovery quality of the first sludge recovery control group according to the recovery quality prediction model to obtain the second sludge recovery control group includes: According to the first group of sludge recovery control, extracting the qth sludge recovery control scheme, where q is a positive integer; Inputting the qth sludge recovery control scheme and the sludge detection data into the recovery quality prediction model to obtain Q recovery quality prediction coefficients, the recovery quality prediction model including Q sludge recovery quality prediction layers, where Q is a positive integer greater than 1; Calculating the mean of the Q recovery quality prediction coefficients to generate a qth sludge recovery quality coefficient, and determining whether the qth sludge recovery quality coefficient is greater than or equal to a sludge recovery quality threshold; If the qth sludge recovery quality coefficient is greater than or equal to the sludge recovery quality threshold, the qth sludge recovery control scheme is added to the second sludge recovery control group.
6. A sludge purification and recovery control method according to claim 1, characterized in that: The recycling mutation optimization mechanism includes: Calculate the sludge recovery quality ratio according to each sludge recovery control scheme in the second sludge recovery control group to obtain the first round of variation value distribution; mutating the second group of sludge recovery control according to the first round of variation value distribution to obtain a first round of recovery variation space; Performing recycling quality optimization on the first-round recycling variation space according to the recycling quality prediction model to obtain a first-round variation optimization space; The second sludge recovery control group is expanded according to the first round of mutation optimization space, and the recovery mutation optimization is continued according to the first round of mutation optimization space until the number of mutation optimization rounds reaches the predetermined number of mutation optimization rounds, and the third sludge recovery control group is generated.
7. A sludge purification and recovery control method according to claim 1, characterized in that: The multi-degree-of-freedom joint optimization is performed on the third group of the sludge recovery control to obtain the sludge recovery control optimization strategy, including: Performing traversal optimization on the third group of sludge recovery control according to a predetermined sludge recovery efficiency, and establishing a fourth group of sludge recovery control having an efficiency greater than or equal to the predetermined sludge recovery efficiency; traversing and optimizing the third group of sludge recovery control according to a predetermined sludge recovery cost, and establishing a fifth group of sludge recovery control having a cost less than or equal to the predetermined sludge recovery cost; Intersection identification is performed based on the fourth sludge recovery control group and the fifth sludge recovery control group to generate the sludge recovery control optimization strategy.
8. A sludge purification and recovery control method according to claim 1, characterized in that: The multivariate detection indicators include sludge type, sludge rheology, sludge solid concentration, unit volume mass, particle size characteristics, floc structure characteristics, sludge moisture content, pH value, nutrients, heavy metals, organic pollutants and microorganisms.
9. A sludge purification and recovery control system, characterized in that: The system is used to implement a sludge purification and recovery control method according to any one of claims 1 to 8, and the system comprises: A detection module, used to detect the sludge to be treated based on multiple detection indicators and obtain sludge detection data; The control variable configuration module is used to configure control variables according to the sludge purification and recovery process and build a multi-stage recovery control variable chain; An association analysis module, configured to perform association analysis on the sludge detection data according to the multi-order recovery control variable chain to obtain an association detection sequence for each order of recovery; A collaborative analysis module, configured to perform a multi-stage recovery control collaborative analysis on the multi-stage recovery control variable chain according to the respective recovery association detection sequences, and establish a first sludge recovery control group; an optimization module, configured to optimize the recovery quality of the first sludge recovery control group according to a recovery quality prediction model to obtain a second sludge recovery control group; The optimization strategy determination module is used to introduce a recycling variation optimization mechanism to expand the second sludge recovery control group to obtain a third sludge recovery control group, and perform multi-degree-of-freedom joint optimization on the third sludge recovery control group to obtain a sludge recovery control optimization strategy.
10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement a sludge purification and recovery control method according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.