A sludge dewatering, drying and incineration collaborative disposal system and method for a sewage treatment plant

Abstract

The present invention relates to the technical field of sludge treatment, and specifically to a sludge dewatering, drying and incineration collaborative disposal system and method for a sewage treatment plant. Sludge data in different areas of a sludge pool are obtained, and corresponding proportions of flocculants and regulators are added to obtain first sludge and data. A sludge parameter prediction model is constructed to identify the first data, and a first maximum water content and a maximum incineration water content are obtained. The first sludge is processed to the first maximum water content to obtain second sludge and data. The sludge parameter prediction model identifies the second data to obtain a first heating temperature range for drying. The second sludge is processed according to the first heating temperature range to obtain third sludge and data. The sludge parameter prediction model identifies the third data to obtain an incineration temperature range. The third sludge is processed to the maximum incineration water content according to the incineration temperature range, and heat energy is recovered. The heat energy is used for the next-stage sludge collaborative treatment. The incineration heat energy is compared with the heat energy for sludge collaborative treatment to determine whether external heat energy is input.

Description

Technical Field

[0001] The present invention relates to the technical field of sludge treatment, and particularly to a sludge dewatering, drying and incineration co-disposal system and method for a sewage treatment plant. Background Art

[0002] A large amount of sludge is generated during the production process of the copper smelting industry. These sludges contain high concentrations of metals (such as copper, lead, zinc, cadmium, and arsenic, etc.) and other complex components. The efficient co-treatment of processes such as dewatering, drying, and incineration of these sludges not only affects the operating costs of sewage treatment plants but also directly relates to the control of environmental pollution and the sustainable utilization of resources. Traditional sludge treatment technologies often have problems such as high energy consumption, unstable treatment effects, and relatively large pollution emissions.

[0003] Existing sludge dewatering technologies mainly rely on methods such as physical pressure filtration and centrifugation. However, these methods require a large amount of mechanical energy consumption, and there is a relatively large amount of residual moisture generated during the treatment process. The sludge drying process uses thermal drying. Although it can significantly reduce the moisture content of the sludge, the temperature control requirements during the drying process are relatively high, and the energy efficiency is relatively low. Sludge incineration is a common method for harmless treatment of sludge, but its incineration process requires a large amount of energy consumption. Therefore, there is an urgent need for an efficient, low-consumption, and environmentally friendly copper smelting sludge treatment system to achieve the synergy between the three processes of dewatering, drying, and incineration, and maximize the treatment benefits through cascaded energy utilization and resource recovery.

[0004] Therefore, a sludge dewatering, drying and incineration co-disposal system and method for a sewage treatment plant are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a sludge dewatering, drying and incineration co-disposal system and method for a sewage treatment plant, which obtains sludge data in different areas of a sludge pool, adds flocculants and regulators in corresponding proportions to obtain first sludge and first sludge data; constructs a sludge parameter prediction model to identify the first sludge data to obtain a first maximum water content and an incineration maximum water content; processes the first sludge to the first maximum water content to obtain second sludge and second sludge data; identifies the second sludge data through the sludge parameter prediction model to obtain a first heating temperature range for sludge drying; processes the second sludge according to the first heating temperature range to obtain third sludge and third sludge data; identifies the third sludge data through the sludge parameter prediction model to obtain a sludge incineration temperature range; processes the third sludge to the incineration maximum water content according to the sludge incineration temperature range and recovers the incineration heat energy; uses the incineration heat energy for the next-stage sludge co-treatment; the heat energy required for the next-stage sludge co-treatment process is first heat energy; compares the incineration heat energy with the first heat energy to determine whether external heat energy input is required.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A sludge dewatering, drying and incineration collaborative disposal system for a sewage treatment plant, comprising:

[0008] A sludge sample analysis unit, which obtains sludge data in different areas of the sludge pool; based on the sludge data, corresponding proportions of flocculant and regulator are added to the sludge pool to obtain first sludge and first sludge data;

[0009] A sludge dewatering unit, which constructs a sludge parameter prediction model to identify the first sludge data, and obtains the first maximum water content after dewatering treatment and the maximum water content for incineration; the first sludge is processed to the first maximum water content to obtain second sludge and second sludge data;

[0010] A sludge drying unit, which identifies the second sludge data through the sludge parameter prediction model, and obtains the first heating temperature range during sludge drying; the second sludge is dried according to the first heating temperature range during second sludge drying to obtain third sludge and third sludge data;

[0011] A sludge incineration unit, which identifies the third sludge data through the sludge parameter prediction model, and obtains the sludge incineration temperature range; the third sludge is incinerated according to the third sludge incineration temperature range to the maximum water content for incineration, and the incineration heat energy generated during the incineration process is obtained;

[0012] A heat energy reuse unit, which uses the incineration heat energy for the next-stage sludge collaborative treatment; the heat energy required for the collaborative treatment process of the next-stage sludge is the first heat energy; the incineration heat energy is compared with the first heat energy to determine whether external heat energy input is required; the judgment calculation formula is:

[0013] ;

[0014] Wherein, is the required external heat energy, is the first heat energy, is the incineration heat energy;

[0015] When External energy supplementation is required; External energy supplementation is not required.

[0016] Preferably, historical data of a copper smelting industrial sludge pool is obtained, and a sludge parameter prediction model is constructed through the historical data;

[0017] The historical data includes sludge volume, initial sludge water content, water content after sludge dewatering, water content after sludge drying, types of heavy metals, heavy metal content, dewatering temperature, drying temperature, incineration temperature, oxygen content, pH value and timestamp;

[0018] Train the sludge parameter prediction model with the historical data;

[0019] The training process is as follows:

[0020] Data preprocessing: Normalize all data to obtain a first preprocessed set;

[0021] Dataset division: Divide the first preprocessed set into a training set and a test set according to a ratio of 7:3;

[0022] Train the model: Input the training set into the sludge parameter prediction model until the model training stop condition is met to obtain an initial prediction model; The model training stop condition is that the energy required after sludge dewatering, drying, and incineration is the least;

[0023] Test the model: Input the test set into the initial prediction model for testing, and optimize the initial prediction model according to the test results to obtain an optimal prediction model.

[0024] Preferably, the sludge data is obtained from sludge samples in different regions;

[0025] The process of obtaining the sludge samples is as follows:

[0026] Perform zoning operations on the copper smelting industrial sludge pond, and divide the sludge pond into sampling areas according to depth and surface area;

[0027] Divide the sludge pond into 3 regions on average according to depth and 9 regions according to surface area to obtain 27 sludge samples;

[0028] Analyze the sludge samples to obtain first sludge data corresponding to different regions; The first sludge data includes heavy metal types, heavy metal contents, and sludge water contents;

[0029] Obtain the sludge data of the sludge pond by performing weighted averaging on the first sludge data in different regions.

[0030] Preferably, the sludge parameter prediction model includes a sludge dehydration volume prediction module and a sludge dehydration temperature prediction module;

[0031] The sludge dehydration volume prediction module includes a first parameter input layer, a data analysis layer, a time series analysis layer, and a water content result output layer;

[0032] The parameter input layer preprocesses the sludge data, flocculant ratio, and regulator ratio to obtain a first standard dataset; The preprocessing includes data cleaning and data normalization;

[0033] The data analysis layer identifies the first standard data set to obtain the characteristics of the types and contents of heavy metals in the sludge and their effects on the dehydration effect; the characteristics are non-linear relationships;

[0034] The time series analysis layer uses a long short-term memory network to obtain time-dependent associations during the sludge dehydration process;

[0035] The water content result output layer analyzes the characteristics and the time-dependent associations to obtain the first maximum water content and the maximum incineration water content after the dehydration treatment.

[0036] Preferably, the sludge dehydration temperature prediction module includes a second parameter input layer, a process classification layer, and a temperature prediction layer;

[0037] The second parameter input layer preprocesses the sludge data, sludge calorific value, ambient temperature, and humidity to obtain a second standard data set;

[0038] The process classification layer analyzes the second standard data set by obtaining the upcoming treatment steps of the sludge to obtain temperature ranges; the temperature ranges are the drying temperature range and the incineration temperature range;

[0039] The temperature prediction layer obtains a temperature range based on the temperature range and the second standard data set.

[0040] Preferably, using the incineration heat energy for the next-stage sludge co-treatment process includes:

[0041] Obtain the water content thresholds and temperature ranges required for the next-stage sludge drying and incineration;

[0042] Monitor the sludge drying and incineration processes in real time to obtain the real-time temperatures of different regions of the sludge;

[0043] Obtain the first heat energy when the sludge drying and incineration reach the water content thresholds and temperature ranges;

[0044] Compare the incineration heat energy with the first heat energy. If the incineration heat energy is less than the first heat energy, external heat energy input is required to make the sludge reach the water content thresholds and temperature ranges.

[0045] Preferably, the specific calculation formula for the first heat energy is:

[0046] ;

[0047] Where, is the first heat energy, is the sludge mass, is the initial specific heat capacity of the sludge, is the total number of heavy metals, is the The mass fraction of a heavy metal is the specific heat correction value of the th heavy metal, is the temperature difference for the sludge to rise from the current temperature to the target temperature, is the latent heat conversion temperature difference required for the sludge during the evaporation process, is the thermal effect coefficient of the heavy metal,

[0048] The specific calculation formula for the incineration heat energy is as follows:

[0049] ;

[0050] wherein, is the incineration heat energy, is the mass flow rate of the heat energy, is the specific heat capacity of the heat steam, is the temperature difference for the heat energy gradient cooling, is the basic heat utilization efficiency, is the efficiency adjustment coefficient, is the current temperature of the sludge, is the target temperature of the sludge.

[0051] A method for co-disposing sludge dewatering, drying and incineration in a sewage treatment plant includes:

[0052] Obtain sludge data in different areas of the sludge pool; Based on the sludge data, add corresponding proportions of flocculant and regulator to the sludge pool to obtain first sludge and first sludge data;

[0053] Construct a sludge parameter prediction model to identify the first sludge data, and obtain the first maximum water content after dehydration treatment and the maximum water content for incineration; Treat the first sludge to the first maximum water content to obtain second sludge and second sludge data;

[0054] Identify the second sludge data through the sludge parameter prediction model to obtain the first heating temperature range during sludge drying; According to the first heating temperature range during the second sludge drying, dry the second sludge to obtain third sludge and third sludge data;

[0055] Identify the third sludge data through the sludge parameter prediction model to obtain the sludge incineration temperature range; According to the third sludge incineration temperature range, incinerate the third sludge to the maximum water content for incineration, and obtain the incineration heat energy generated during the incineration process;

[0056] Use the heat energy generated from incineration for the sludge co-treatment process in the next stage; the heat energy required for the sludge co-treatment process in the next stage is the first heat energy; compare the incineration heat energy with the first heat energy to determine whether external heat energy input is required; the judgment calculation formula is:

[0057] ;

[0058] where, is the required external heat energy, is the first heat energy, is the incineration heat energy;

[0059] When , external energy supplementation is required; , external energy supplementation is not required.

[0060] Preferably, obtain the historical data of the copper smelting industrial sludge pond, and construct a sludge parameter prediction model through the historical data;

[0061] The historical data includes sludge volume, initial moisture content of sludge, moisture content after sludge dewatering, moisture content after sludge drying, types of heavy metals, heavy metal content, dewatering temperature, drying temperature, incineration temperature, oxygen content, pH value, and timestamp;

[0062] Train the sludge parameter prediction model through the historical data.

[0063] Preferably, using the incineration heat energy for the sludge co-treatment process in the next stage includes:

[0064] Obtain the moisture content threshold and temperature range required for sludge drying and incineration in the next stage;

[0065] Monitor the sludge drying and incineration processes in real time, and obtain the real-time temperature of different regions of the sludge;

[0066] Obtain the first heat energy when the sludge drying and incineration reach the moisture content threshold and temperature range;

[0067] Compare the incineration heat energy with the first heat energy. If the incineration heat energy is less than the first heat energy, external heat energy input is required to make the sludge reach the moisture content threshold and temperature range.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1. The present invention analyzes the physical and chemical properties of sludge in different areas of a copper smelting industrial sludge pond by conducting zonal sampling on the sludge pond, dividing the sludge pond into multiple areas according to depth and surface area, obtaining 27 sludge samples, and combining a detailed analysis of the types of heavy metals, heavy metal content, and sludge water content. The overall sludge data of the sludge pond is obtained through weighted average calculation, and based on this, the addition ratios of flocculants and regulators are optimized, not only improving the efficiency of flocculation and conditioning, but also reducing the treatment cost.

[0070] 2. The present invention constructs a sludge parameter prediction model, which uses the historical data and real-time data of a copper smelting industrial sludge pond to predict and optimize the processes of sludge dewatering, drying, and incineration. By analyzing multi-dimensional parameters of sludge data, including types of heavy metals, heavy metal content, initial water content, sludge calorific value, environmental temperature, and humidity, etc., the maximum water content during incineration when the sludge incineration reaches the best conditions is predicted, and the optimal temperature ranges during drying and incineration processes are obtained. By predicting the minimization of energy utilization during the drying and incineration processes of sludge, the efficiency of the treatment process is ensured to be high and energy-saving, and at the same time, the reduction of treatment efficiency or waste of resources caused by improper temperature or humidity settings is reduced.

[0071] 3. The present invention realizes the recycling of thermal energy and the optimized control of energy consumption by efficiently recovering the thermal energy generated during the incineration process and applying it to the co-treatment of sludge in the next stage. By comparing the incineration thermal energy with the first thermal energy required for the co-treatment of sludge drying and incineration in the next stage in real time, and combining the thermal energy calculation formula to accurately judge whether external thermal energy input is needed, and by calculating the matching relationship between the incineration thermal energy and the first thermal energy, it is ensured that the temperature and water content can reach the set thresholds during the co-treatment of sludge, realizing the stability and high efficiency of the sludge treatment process, thus effectively reducing the dependence on external energy and improving the overall energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic structural diagram of a co-disposal system for sludge dewatering, drying, and incineration in a sewage treatment plant provided by the present invention;

[0073] Figure 2 It is a schematic flow diagram of a method for co-disposing sludge dewatering, drying, and incineration in a sewage treatment plant provided by the present invention;

[0074] Figure 3 It is a schematic structural diagram of a sludge parameter prediction model provided by the present invention;

[0075] Figure 4 It is a schematic structural diagram of a sludge water content prediction module provided by the present invention;

[0076] Figure 5 It is a schematic structural diagram of a sludge dewatering temperature prediction module provided by the present invention. Detailed implementation mode

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] Embodiment 1

[0079] In order to achieve efficient collaborative control of sludge dewatering, drying and incineration, the present invention introduces a sludge parameter prediction model. By analyzing the historical data of the copper smelting industrial sludge pool, the key parameters in the sludge treatment process are accurately predicted. These historical data cover multiple key links and environmental characteristics of sludge treatment, providing a sufficient data basis for the construction of the model. The present invention provides a collaborative disposal system for sludge dewatering, drying and incineration in a sewage treatment plant. For the specific system structure diagram, refer to Figure 1 .

[0080] Furthermore, obtain the historical data of the copper smelting industrial sludge pool, and construct a sludge parameter prediction model through the historical data;

[0081] The historical data includes sludge volume, initial moisture content of sludge, moisture content after sludge dewatering, moisture content after sludge drying, types of heavy metals, heavy metal content, dewatering temperature, drying temperature, incineration temperature, oxygen content, pH value and time stamp;

[0082] Train the sludge parameter prediction model through the historical data;

[0083] The training process is as follows:

[0084] Data preprocessing: Perform normalization processing on all data to obtain a first preprocessing set;

[0085] Dataset division: Divide the first preprocessing set into a training set and a test set according to a ratio of 7:3;

[0086] Train the model: Input the training set into the sludge parameter prediction model until the model training stop condition is met to obtain an initial prediction model; the model training stop condition is that the energy required after sludge dewatering, drying and incineration is the least;

[0087] Test the model: Input the test set into the initial prediction model for testing, and optimize the initial prediction model according to the test results to obtain an optimal prediction model.

[0088] In this embodiment, by using the historical data of the copper smelting industrial sludge pool to construct a sludge parameter prediction model, the prediction and optimization of the sludge treatment process are realized. The sludge parameter prediction model reduces the energy consumption in the treatment process by comprehensively analyzing multi-dimensional data such as sludge volume, initial water content, heavy metal types and contents, dewatering temperature, drying temperature, incineration temperature, etc.

[0089] The sludge sample analysis unit obtains sludge data in different areas of the sludge pool; based on the sludge data, a corresponding proportion of flocculant and regulator is added to the sludge pool to obtain the first sludge and the first sludge data;

[0090] Furthermore, the sludge data is obtained by acquiring sludge samples in different areas;

[0091] The process of obtaining the sludge samples is as follows:

[0092] Perform zoning operations on the copper smelting industrial sludge pool, and divide the sludge pool into sampling areas according to depth and surface area;

[0093] It is evenly divided into 3 areas according to the depth of the sludge pool and 9 areas according to the surface area, obtaining 27 sludge samples;

[0094] By analyzing the sludge samples, the first sludge data corresponding to different areas is obtained; the first sludge data includes heavy metal types, heavy metal contents, and sludge water content;

[0095] By performing weighted averaging on the first sludge data in different areas, the sludge data of the sludge pool is obtained;

[0096] The weighted average formula is:

[0097] ;

[0098] Where is the average content of the th heavy metal element in the sludge pool, is the weight of the th area, is the content of the th area and the th heavy metal element, is the total number of areas, and in this embodiment is 27.

[0099] In this embodiment, sludge data of different regions in the sludge pool is obtained through the partition sampling and data analysis method of the sludge sample analysis unit. By dividing the sludge pool into multiple sampling regions according to depth and surface area, the comprehensiveness and representativeness of sample collection are ensured, avoiding data deviation problems that may be caused by traditional sampling methods. By analyzing key parameters such as the types of heavy metals, heavy metal contents, and sludge water contents in sludge samples from each region and performing weighted average processing on them, the overall sludge data of the sludge pool can be effectively obtained. This provides a scientific basis for adjusting the proportions of flocculants and regulators in the subsequent sludge treatment process and reduces the usage amounts of flocculants and regulators.

[0100] By obtaining the types and contents of heavy metals in the sludge pool of copper smelting factory A, analyzing the mass fractions of the main heavy metals (copper, lead, zinc, cadmium, arsenic) in the sludge in the sludge pool and their influence laws on the demands for flocculants and regulators, the relationship between the changes in the contents of each heavy metal and the demands for flocculants and regulators is determined. Table 1 shows the data on the influence of typical heavy metal contents in the sludge of factory A on the demands for flocculants and regulators;

[0101] Table 1 Table of heavy metal contents and flocculant and regulator ratios

[0102]

[0103] It can be found from Table 1 that the contents of copper, lead, zinc, cadmium, and arsenic have a greater influence on the demand for flocculants. As the contents increase, the usage amount of flocculants increases rapidly; the usage amount of the regulator is relatively low because its main function is to adjust the pH value or stabilize heavy metal ions. Even when the heavy metal content is relatively high, the increase in the demand for the regulator is relatively small.

[0104] The sludge dewatering unit constructs a sludge parameter prediction model to identify the first sludge data, obtaining the first maximum water content after dewatering treatment and the maximum water content for incineration; the first sludge is processed to the first maximum water content to obtain the second sludge and the second sludge data; where the maximum water content for incineration means that no additional auxiliary fuel needs to be added;

[0105] The sludge drying unit identifies the second sludge data through the sludge parameter prediction model, obtaining the first heating temperature range during sludge drying; the second sludge is dried according to the first heating temperature range during the second sludge drying to obtain the third sludge and the third sludge data;

[0106] The sludge incineration unit identifies the third sludge data through the sludge parameter prediction model, obtaining the sludge incineration temperature range; the third sludge is incinerated according to the third sludge incineration temperature range to the maximum water content for incineration, and the incineration heat energy generated during the incineration process is obtained;

[0107] Further, the sludge parameter prediction model includes a sludge water content prediction module and a sludge dehydration temperature prediction module. Refer to Figure 3 ;

[0108] The sludge water content prediction module includes a first parameter input layer, a data analysis layer, a time series analysis layer, and a water content result output layer. Refer to Figure 4 ;

[0109] The parameter input layer preprocesses the sludge data, flocculant ratio, and regulator ratio to obtain a first standard data set. The preprocessing includes data cleaning and data normalization;

[0110] The data analysis layer identifies the characteristics of the types and contents of heavy metals in the sludge on the dehydration effect from the first standard data set. The characteristics are non-linear relationships;

[0111] The time series analysis layer obtains the time-dependent associations during the sludge dehydration process by using a long short-term memory network;

[0112] The water content result output layer analyzes the characteristics and the time-dependent associations to obtain the first maximum water content after dehydration treatment and the maximum water content for incineration.

[0113] In this embodiment, the sludge dehydration, drying, and incineration collaborative treatment system provided by the present invention can identify and predict the key parameters of sludge during dehydration, drying, and incineration by constructing a sludge parameter prediction model. By combining information such as sludge data, flocculant ratio, and regulator ratio, the system can optimize the sludge treatment process and obtain the accurate maximum water content after dehydration and the maximum water content for incineration. In addition, by using a long short-term memory network (LSTM) to analyze the time-dependent relationships during the sludge dehydration process, dynamic adjustment can be realized to ensure high efficiency and minimum energy consumption during the treatment process.

[0114] Predict the first maximum water content after dehydration treatment and the maximum water content for incineration for the first sludge data of Factory A according to the sludge water content prediction module in the sludge parameter prediction model. Some data are shown in Table 2;

[0115] Table 2 Data table of the influence of heavy metal types and contents on the water content after dehydration and the water content during incineration

[0116]

[0117] Further, the sludge dehydration temperature prediction module includes a second parameter input layer, a process classification layer, and a temperature prediction layer. Refer to Figure 5 ;

[0118] The second parameter input layer preprocesses sludge data, sludge calorific value, environmental temperature, and humidity to obtain a second standard data set;

[0119] The process classification layer analyzes the second standard data set by obtaining the upcoming sludge treatment steps to obtain temperature ranges; the temperature ranges are the drying temperature range and the incineration temperature range;

[0120] The temperature prediction layer obtains a temperature range based on the temperature range and the second standard data set.

[0121] Predict the temperature ranges during drying and incineration for the first sludge data of Factory A according to the sludge dehydration temperature prediction module in the sludge parameter prediction model. Some data are shown in Table 3;

[0122] Table 3 Data table of the influence of heavy metal types and contents on the water content after dehydration and the water content during incineration

[0123]

[0124] Among them, the drying temperature range gradually rises as the heavy metal content increases. The main reason is that high-concentration heavy metals affect the binding state of water in the sludge; as the heavy metal concentration increases, the incineration temperature range slightly increases, and the incineration temperature requirement is slightly increased to ensure the effective solidification of heavy metals and the combustion efficiency.

[0125] In this embodiment, the temperature prediction module accurately predicts the temperature ranges during drying and incineration by analyzing factors such as sludge calorific value, environmental temperature, and humidity, and optimizes energy use. The implementation of this system not only improves the sludge treatment efficiency but also effectively reduces energy consumption and environmental pollution.

[0126] The thermal energy reuse unit uses the incineration thermal energy for the next-stage sludge co-treatment; the thermal energy required for the next-stage sludge co-treatment process is the first thermal energy; compare the incineration thermal energy with the first thermal energy to determine whether external thermal energy input is required; the judgment calculation formula is:

[0127] ;

[0128] Among them, is the required external thermal energy, is the first thermal energy, is the incineration thermal energy;

[0129] When External energy supplementation is required; External energy supplementation is not required.

[0130] Furthermore, using the incineration thermal energy for the next-stage sludge co-treatment process includes:

[0131] Obtain the moisture content threshold and temperature range required for the next stage of sludge drying and incineration;

[0132] Monitor the sludge drying and incineration process in real time to obtain the real-time temperature of different regions of the sludge;

[0133] Obtain the first thermal energy when the sludge drying and incineration reach the moisture content threshold and temperature range;

[0134] Compare the incineration thermal energy with the first thermal energy. If the incineration thermal energy is less than the first thermal energy, external thermal energy input is required to make the sludge reach the moisture content threshold and temperature range.

[0135] In this embodiment, the thermal energy recycling unit provided by the present invention effectively applies the thermal energy generated during the incineration process to the co-treatment of sludge in the next stage, realizing the recycling of thermal energy. By comparing the incineration thermal energy with the thermal energy required for the next stage of sludge treatment, it is possible to judge in real time whether external thermal energy input is needed, thereby optimizing the energy utilization efficiency. By obtaining the temperature and moisture content information during the sludge drying and incineration processes and combining the real-time monitoring data, the system can adjust the thermal energy input, reduce unnecessary energy waste, lower energy consumption, and improve the overall treatment efficiency of the system.

[0136] Further, the specific calculation formula for the first thermal energy is:

[0137] ;

[0138] Where is the specific heat capacity of the sludge, is the correction term for the influence of heavy metal content on the heat demand of the sludge during evaporation and drying;

[0139] Where is the first thermal energy, is the mass of the sludge, is the initial specific heat capacity of the sludge, is the total number of heavy metals, is the th mass fraction of the th heavy metal, is the specific heat correction value of the th heavy metal, is the temperature difference of the sludge rising from the current temperature to the target temperature, is the latent heat conversion temperature difference required for the sludge during evaporation, is the thermal effect coefficient of the heavy metal,

[0140] The specific calculation formula for the incineration thermal energy is:

[0141] ;

[0142] Among them, is the thermal energy utilization efficiency;

[0143] Among them, is the incineration thermal energy, is the mass flow rate of thermal energy, is the specific heat capacity of heat steam, is the temperature difference of thermal energy gradient cooling, is the basic thermal utilization efficiency, is the efficiency adjustment coefficient, is the current temperature of the sludge, is the target temperature of the sludge.

[0144] In this embodiment, through the accurate calculation and comparison of the first thermal energy and the incineration thermal energy, the thermal energy management in the sludge treatment process is effectively improved. When calculating the first thermal energy, factors such as the specific heat capacity of the sludge, the influence of heavy metal content on heat demand, and temperature changes are considered, so as to realize a more refined prediction of thermal energy demand. The calculation of incineration thermal energy fully considers parameters such as the mass flow rate of thermal energy, steam specific heat capacity, thermal energy gradient, and utilization efficiency, and ensures the reasonable distribution of heat in the sludge treatment process by dynamically adjusting the thermal energy input.

[0145] The present invention provides a sludge dewatering, drying and incineration collaborative disposal system for a sewage treatment plant. By integrating multiple modules such as a sludge sample analysis unit, a sludge dewatering unit, a sludge drying unit, a sludge incineration unit, and a thermal energy reuse unit, it realizes the efficient collaboration and energy optimization in the sludge treatment process. First, the system accurately determines the composition and water content of the sludge by analyzing the sample data in different areas of the sludge pool, providing accurate basic data for the subsequent dewatering, drying and incineration processes. Through the sludge parameter prediction model, it can dynamically predict the maximum water content and temperature range of the sludge in each treatment stage, effectively optimizing the dewatering and incineration processes and reducing energy consumption. The thermal energy reuse module of the present invention can use the thermal energy generated during the incineration process for the next stage of sludge collaborative treatment. By comparing the required thermal energy with the actual incineration thermal energy, it can intelligently judge whether external thermal energy input is needed, thus effectively reducing the dependence on external energy. Through time series analysis technologies such as long short-term memory network (LSTM), it predicts and adjusts the temperature and water content of the sludge during the dewatering process to ensure the optimal treatment effect. The implementation of this system not only improves the energy efficiency and resource utilization rate of sludge treatment, but also has significant advantages in environmental protection and cost control, and has broad application prospects.

[0146] Embodiment 2

[0147] With the acceleration of the urbanization process, the problem of sludge treatment in sewage treatment plants has become increasingly serious. Traditional methods of sludge dewatering, drying, and incineration have problems such as high energy consumption and poor treatment effects. Existing technologies mainly focus on a single treatment link and lack effective co-disposal methods, resulting in energy waste and secondary pollution during the sludge treatment process. To solve these problems, the present invention proposes a co-disposal method for sludge dewatering, drying, and incineration in a sewage treatment plant. The specific method flow chart is referred to Figure 2 ;

[0148] Training the sludge parameter prediction model through the historical data;

[0149] The training process is as follows:

[0150] Data preprocessing: Normalize all data to obtain a first preprocessing set;

[0151] Dataset division: Divide the first preprocessing set into a training set and a test set according to a ratio of 7:3;

[0152] Training the model: Input the training set into the sludge parameter prediction model until the model training stop condition is met to obtain an initial prediction model; the model training stop condition is that the energy required after sludge dewatering, drying, and incineration is the least;

[0153] Testing the model: Input the test set into the initial prediction model for testing, and optimize the initial prediction model according to the test results to obtain an optimal prediction model.

[0154] Refer to Figure 2 of S10, obtain sludge data in different areas of the sludge pool; based on the sludge data, add corresponding proportions of flocculants and regulators to the sludge pool to obtain first sludge and first sludge data;

[0155] Furthermore, the sludge data is obtained by acquiring sludge samples in different areas;

[0156] The process of obtaining the sludge samples is as follows:

[0157] Perform zoning operations on the copper smelting industrial sludge pool, and divide the sludge pool into sampling areas according to depth and surface area;

[0158] Divide the sludge pool into 3 areas on average according to depth and 9 areas according to surface area to obtain 27 sludge samples;

[0159] By analyzing the sludge samples, obtain first sludge data corresponding to different areas; the first sludge data includes heavy metal types, heavy metal contents, and sludge water contents;

[0160] The sludge data of the sludge pool is obtained by weighted averaging the first sludge data of different regions;

[0161] The weighted average formula is:

[0162] ;

[0163] Wherein, is the average content of the th heavy metal element in the sludge pool, is the weight of the th region, is the content of the th heavy metal element in the th region, is the total number of regions, which is 27 in this embodiment.

[0164] Referring to S20 in Figure 2 , a sludge parameter prediction model is constructed to identify the first sludge data, and the first maximum water content after dehydration treatment and the maximum water content for incineration are obtained; the first sludge is processed to the first maximum water content to obtain the second sludge and the second sludge data; wherein the maximum water content for incineration means that no additional auxiliary fuel needs to be added;

[0165] Referring to S30 in Figure 2 , the second sludge data is identified through the sludge parameter prediction model to obtain the first heating temperature range during sludge drying; the second sludge is dried according to the first heating temperature range during second sludge drying to obtain the third sludge and the third sludge data;

[0166] Referring to S40 in Figure 2 , the third sludge data is identified through the sludge parameter prediction model to obtain the sludge incineration temperature range; the third sludge is incinerated according to the third sludge incineration temperature range to the maximum water content for incineration, and the incineration heat energy generated during the incineration process is obtained;

[0167] Furthermore, the sludge parameter prediction model includes a sludge water content reduction prediction module and a sludge dehydration temperature prediction module;

[0168] The sludge water content reduction prediction module includes a first parameter input layer, a data analysis layer, a time series analysis layer, and a water content result output layer;

[0169] The parameter input layer preprocesses the sludge data, the flocculant ratio, and the regulator ratio to obtain a first standard data set; the preprocessing includes data cleaning and data normalization;

[0170] The data analysis layer identifies the first standard data set to obtain the characteristics of the types and contents of heavy metals in the sludge and their effects on the dewatering efficiency; the characteristics are non-linear relationships;

[0171] The time series analysis layer uses a long short-term memory network to obtain time-dependent associations during the sludge dewatering process;

[0172] The water content result output layer analyzes the characteristics and the time-dependent associations to obtain the first maximum water content and the maximum incineration water content after the dewatering treatment.

[0173] Furthermore, the sludge dewatering temperature prediction module includes a second parameter input layer, a process classification layer, and a temperature prediction layer;

[0174] The second parameter input layer preprocesses sludge data, sludge calorific value, ambient temperature, and humidity to obtain a second standard data set;

[0175] The process classification layer analyzes the second standard data set by obtaining the upcoming treatment steps of the sludge to obtain temperature ranges; the temperature ranges are the drying temperature range and the incineration temperature range;

[0176] The temperature prediction layer obtains a temperature range based on the temperature ranges and the second standard data set.

[0177] Refer to Figure 2 of S50, and use the incineration heat energy for the co-treatment of sludge in the next stage; the heat energy required for the co-treatment process of the sludge in the next stage is the first heat energy; compare the incineration heat energy with the first heat energy to determine whether external heat energy input is required; the judgment calculation formula is:

[0178] ;

[0179] Wherein, is the required external heat energy, and its unit is KJ, is the first heat energy, is the incineration heat energy;

[0180] When External energy supplementation is required; External energy supplementation is not required.

[0181] Furthermore, using the incineration heat energy for the co-treatment process of sludge in the next stage includes:

[0182] Obtain the water content threshold and temperature range required for the drying and incineration of the sludge in the next stage;

[0183] Monitor the drying and incineration processes of the sludge in real time to obtain the real-time temperatures of different regions of the sludge;

[0184] Obtain the first thermal energy when the sludge drying and incineration reach the water content threshold and temperature range;

[0185] Compare the incineration thermal energy with the first thermal energy. If the incineration thermal energy is less than the first thermal energy, external thermal energy input is required to make the sludge reach the water content threshold and temperature range.

[0186] Further, the specific calculation formula for the first thermal energy is:

[0187] ;

[0188] Wherein, is the first thermal energy, with the unit of KJ, is the sludge mass, with the unit of kg, is the initial specific heat capacity of the sludge, is the total number of heavy metals, is the mass fraction of the th heavy metal, is the specific heat correction value of the th heavy metal, is the temperature difference between the current temperature and the target temperature of the sludge rising, with the unit of °C, is the latent heat conversion temperature difference required during the evaporation process of the sludge, with the unit of °C, is the thermal effect coefficient of the heavy metal, is the total heavy metal content (mass percentage); is the specific heat capacity of the sludge, with the unit of is the temperature difference of the thermal energy gradient cooling, with the unit of °C, is the basic thermal utilization efficiency, is the efficiency adjustment coefficient, is the current temperature of the sludge, with the unit of °C, is the target temperature of the sludge, with the unit of °C, is the thermal energy utilization efficiency;

[0189] The present invention provides a method for coordinated treatment of sludge dewatering, drying and incineration in a sewage treatment plant, which improves the energy utilization efficiency and reduces the treatment cost by optimizing the sludge treatment process. The sludge parameter prediction model of the present invention is trained with historical data and combines multi-dimensional information such as the type, content, and temperature of the sludge to accurately predict the energy requirements at each treatment stage. During the sludge drying and incineration process, the temperature change of the sludge is monitored in real time to ensure that the treatment reaches the predetermined water content threshold and temperature range, thereby optimizing the utilization of thermal energy and the judgment of external thermal energy input. The overall system significantly improves the energy utilization rate and operation efficiency of sludge treatment.

[0190] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sludge dehydration, drying and incineration coordinated disposal system for a sewage treatment plant, characterized in that: include: Sludge sample analysis unit, to obtain sludge data from different areas in the sludge pool; Based on the sludge data, flocculants and regulators of corresponding proportions are added to the sludge pool to obtain the first sludge and the first sludge data; the sludge pool is evenly divided into 3 areas according to the depth and divided into 9 areas according to the surface area, and 27 sludge samples are obtained; The sludge dehydration unit constructs a sludge parameter prediction model to identify the first sludge data, and obtains the first maximum moisture content after dehydration treatment and the maximum moisture content during incineration; treating the first sludge to a first maximum water content to obtain a second sludge and second sludge data; The sludge drying unit identifies the second sludge data through the sludge parameter prediction model to obtain the first heating temperature range when the sludge is dried; the second sludge is dried according to the first heating temperature range when the second sludge is dried to obtain the third sludge and the third sludge data; The sludge incineration unit identifies the third sludge data through the sludge parameter prediction model to obtain the sludge incineration temperature range; incinerates the third sludge to the maximum incineration moisture content according to the third sludge incineration temperature range, and obtains the incineration heat energy generated during the incineration process; The sludge parameter prediction model includes a sludge dehydration amount prediction module and a sludge dehydration temperature prediction module; The sludge dehydration amount prediction module includes a first parameter input layer, a data analysis layer, a time series analysis layer and a moisture content result output layer; The parameter input layer obtains a first standard data set by preprocessing the sludge data, the flocculant ratio and the regulator ratio; The preprocessing includes data cleaning and data normalization; The data analysis layer obtains the characteristics of the relationship between the type and content of heavy metals in the sludge and the dehydration effect by identifying the first standard data set; the characteristics are nonlinear relationships; The time series analysis layer may use a long short-term memory network to obtain time-dependent associations in the sludge dewatering process; The moisture content result output layer obtains the first maximum moisture content after dehydration and the maximum moisture content after incineration by analyzing the characteristics and the time-dependent association; The sludge dehydration temperature prediction module includes a second parameter input layer, a process classification layer and a temperature prediction layer; The second parameter input layer obtains a second standard data set by preprocessing sludge data, sludge calorific value, ambient temperature and humidity; The process classification layer obtains the sludge processing step to be processed, analyzes the second standard data set, and obtains the temperature range; The temperature range is a drying temperature range and an incineration temperature range; The temperature prediction layer obtains a temperature range based on the temperature interval and the second standard data set; Heat recycling unit, which uses the incineration heat for the next stage of sludge co-treatment; The heat energy required for the next stage of sludge collaborative treatment is the first heat energy; obtain the first heat energy for sludge drying and incineration to reach the moisture content threshold and temperature range; compare the incineration heat energy with the first heat energy to determine whether external heat energy input is required; the judgment calculation formula is: ； in, is the external heat energy required, is the first heat energy, For incineration heat energy; when Requires external energy supplement; No external energy supplement is required.

2. The sludge dehydration, drying and incineration coordinated disposal system of a sewage treatment plant according to claim 1, characterized in that: Obtaining historical data of copper smelting industrial sludge pools, and constructing a sludge parameter prediction model based on the historical data; The historical data include sludge volume, sludge initial moisture content, sludge moisture content after dehydration, sludge moisture content after drying, heavy metal types, heavy metal content, dehydration temperature, drying temperature, incineration temperature, oxygen content, pH value and timestamp; Training the sludge parameter prediction model using the historical data; The training process is: Data preprocessing: normalize all data to obtain the first preprocessing set; Data set division: the first preprocessed set is divided into a training set and a test set in a ratio of 7:3; Training model: inputting the training set into the sludge parameter prediction model until the model training stop condition is met to obtain an initial prediction model; the model training stop condition is that the energy required for sludge dehydration, drying and incineration is minimal; Testing model: The test set is input into the initial prediction model for testing, and the initial prediction model is optimized according to the test results to obtain the optimal prediction model.

3. The sludge dehydration, drying and incineration coordinated disposal system of a sewage treatment plant according to claim 1, characterized in that: The sludge data is obtained by obtaining sludge samples from different regions; The sludge sample acquisition process is as follows: The copper smelting industry sludge pool was zoned and the sampling area was divided according to the depth and surface area; The sludge pool was divided into 3 areas according to its depth and 9 areas according to its surface area, resulting in 27 sludge samples. By analyzing the sludge samples, first sludge data corresponding to different areas are obtained; the first sludge data includes the type of heavy metals, the content of heavy metals and the water content of sludge; The sludge data of the sludge pool is obtained by weighted averaging the first sludge data of different areas.

4. The sludge dehydration, drying and incineration coordinated disposal system of a sewage treatment plant according to claim 1, characterized in that: The incineration heat is used for the next stage of sludge co-treatment process including: Obtain the moisture content threshold and temperature range required for the next stage of sludge drying and incineration; Real-time monitoring of the sludge drying and incineration process to obtain the real-time temperature of different areas of the sludge; Acquiring first heat energy for drying and incinerating the sludge to reach a water content threshold and a temperature range; Comparing the incineration heat energy with the first heat energy, if the incineration heat energy is less than the first heat energy, external heat energy input is required to make the sludge reach the moisture content threshold and temperature range.

5. The sludge dehydration, drying and incineration coordinated disposal system of a sewage treatment plant according to claim 4, characterized in that: The specific calculation formula of the first thermal energy is: ； in, is the first heat energy, is the sludge quality, is the initial specific heat capacity of the sludge, is the total amount of heavy metals, For the The mass fraction of heavy metals, For the Corrected values ​​of specific heat of heavy metals, is the temperature difference of sludge from the current temperature to the target temperature, is the temperature difference required for latent heat conversion during sludge evaporation. is the thermal effect coefficient of heavy metals, is the total content of heavy metals; The specific calculation formula for the incineration heat energy is: ； in, To burn heat energy, is the thermal energy mass flow rate, is the specific heat capacity of thermal steam, is the thermal energy gradient cooling temperature difference, As the basic heat utilization efficiency, is the efficiency adjustment coefficient, is the current temperature of the sludge, is the target sludge temperature.

6. A sludge dehydration, drying and incineration coordinated disposal method for a sewage treatment plant, characterized in that: The sludge dehydration, drying and incineration coordinated disposal system of a sewage treatment plant as claimed in claim 1 comprises: Acquire sludge data of different areas in the sludge pool; add flocculants and regulators in corresponding proportions to the sludge pool based on the sludge data to obtain first sludge and first sludge data; Constructing a sludge parameter prediction model to identify the first sludge data, obtaining a first maximum moisture content after dehydration and a maximum moisture content after incineration; treating the first sludge to the first maximum moisture content, obtaining a second sludge and a second sludge data; The second sludge data is identified by the sludge parameter prediction model to obtain a first heating temperature range for sludge drying; the second sludge is dried according to the first heating temperature range for sludge drying to obtain a third sludge and the third sludge data; The third sludge data is identified by the sludge parameter prediction model to obtain a sludge incineration temperature range; the third sludge is incinerated to the maximum incineration water content according to the third sludge incineration temperature range, and incineration heat energy generated during the incineration process is obtained; The incineration heat energy is used for the next stage of sludge collaborative treatment process; the heat energy required for the next stage of sludge collaborative treatment process is the first heat energy; the incineration heat energy is compared with the first heat energy to determine whether external heat energy input is required; the judgment calculation formula is: ； in, is the external heat energy required, is the first heat energy, For incineration heat energy; when , external energy supplement is required; , no external energy supplement is required.

7. The sludge dehydration, drying and incineration coordinated disposal method of a sewage treatment plant according to claim 6, characterized in that: Obtain historical data of copper smelting industrial sludge pools, and construct a sludge parameter prediction model based on the historical data; The historical data include sludge volume, sludge initial moisture content, sludge moisture content after dehydration, sludge moisture content after drying, heavy metal types, heavy metal content, dehydration temperature, drying temperature, incineration temperature, oxygen content, pH value and timestamp; The sludge parameter prediction model is trained using the historical data.

8. The sludge dehydration, drying and incineration coordinated disposal method of a sewage treatment plant according to claim 6, characterized in that: The incineration heat is used for the next stage of sludge co-treatment process including: Obtain the moisture content threshold and temperature range required for the next stage of sludge drying and incineration; Real-time monitoring of the sludge drying and incineration process to obtain the real-time temperature of different areas of the sludge; Acquiring first heat energy for drying and incinerating the sludge to reach a water content threshold and a temperature range; Comparing the incineration heat energy with the first heat energy, if the incineration heat energy is less than the first heat energy, external heat energy input is required to make the sludge reach the moisture content threshold and temperature range.

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