Automatic control system and method for sludge treatment

Abstract

The invention discloses an automatic control system and method for sludge treatment, and relates to the technical field of sludge treatment, an environment state feature vector F is collected in real time through an integrated sensor, an odor concentration prediction result vector Cpred at different time points in the future is calculated based on a dynamic odor diffusion prediction model, and then the concentration trend is predicted in combination with the prediction result vector Cpred. The decision vector D of the addition amount of the additive is dynamically adjusted, the limitation that in the prior art, addition control is conducted only depending on real-time odor concentration feedback is broken through, and the problem of addition lag is effectively solved through combination of the Gaussian plume diffusion model and the time sequence prediction method. Meanwhile, benefit index E evaluation is carried out through the adjusted concentration vector Cafter and the odor concentration change vector Gafter, so that the addition strategy is optimized based on the concentration change trend, factors such as the use cost of an auxiliary agent and the odor diffusion stability are fully considered, and the defect that the deodorization effect and the cost are difficult to balance by the addition amount in a traditional method is overcome.

Description

Technical Field

[0001] The invention relates to the technical field of sludge treatment, and in particular to an automatic control system and method for sludge treatment. Background Art

[0002] In the field of environmental protection and resource recycling, sewage treatment is one of the important links in urban and industrial development. Sewage treatment has become an important part of urban infrastructure construction. However, a large amount of sludge is produced during sewage treatment. These sludges usually contain organic matter, heavy metals, pathogenic microorganisms, etc., which pose a potential threat to the environment and human health. Therefore, the safe disposal of sludge has become an important issue in the environmental protection industry.

[0003] In the existing sludge treatment automation control system, the deodorization strategy mainly relies on real-time monitoring of odor concentration and then adjusting the dosage, which is a typical feedback control mode. Although this method can control odor to a certain extent, it has many limitations, such as: delayed reaction and untimely deodorization: Since the diffusion of odor has a certain lag, the odor value monitored by the current concentration may already be the result of excessive diffusion. This means that even if the drug is added immediately, the odor cannot be quickly suppressed, and the surrounding environment will still be polluted for a short period of time.

[0004] Waste of chemicals and increased costs: Due to the failure to predict the changing trend of odor in advance, the system often uses a higher dosage to ensure the deodorization effect, resulting in the waste of some chemical reagents, increasing the cost of sludge treatment, and increasing the possibility of secondary pollution of sewage.

[0005] Lack of comprehensive consideration of environmental factors: Current odor control is based only on concentration detection, without considering key factors that affect odor diffusion, such as temperature, humidity, wind speed, and air pressure. For example, in a high temperature and high humidity environment, odor evaporates faster, while in a low temperature and low humidity environment, it diffuses slower. If the dosage is not adjusted in combination with these factors, it is easy to cause problems such as insufficient or excessive dosage.

[0006] Local control makes global optimization difficult: Current deodorization strategies often control a single odor monitoring point without establishing a global diffusion model, making it impossible to predict the odor diffusion trend in different areas of the factory, resulting in high odor concentrations in some areas while excessive dosage in other areas. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an automated control system and method for sludge treatment, which solves the problems mentioned in the background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a sludge treatment automation control method, comprising the following steps:

[0009] S1, collect environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F;

[0010] S2. Based on the acquired environmental state feature vector F, a dynamic odor diffusion prediction model is constructed to calculate the odor concentration change trend at different time points and obtain the odor concentration prediction result vector CP;

[0011] S3, performing calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage;

[0012] S4, simulating the change of odor concentration after applying the decision vector D, and obtaining the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation;

[0013] S5. Perform a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, obtain the benefit index E, and adjust the dosage of the additive according to the benefit index E, obtain the execution decision vector D1, and adaptively adjust the sludge treatment process according to the execution decision vector D1;

[0014] S6. Record the actual odor concentration Cfinal after executing the decision vector D1, calculate the deviation △C from the preset target odor concentration, and trigger the iterative optimization mechanism according to the deviation △C.

[0015] Preferably, said S1 includes S11 and S12;

[0016] S11, collecting environmental information that affects odor diffusion during sludge treatment in real time through integrated multiple sensors, and forming an environmental information vector Fel, wherein the environmental information vector Fel includes temperature T, humidity H, wind speed W, wind direction θ, air pressure P and odor source estimated intensity S;

[0017] Among them, the sludge treatment process includes sludge storage process, conditioning process, dehydration process and stacking process;

[0018] Among them, the various sensors include temperature sensors, humidity sensors, ultrasonic anemometers, wind direction sensors, and air pressure sensors;

[0019] The odor source estimation intensity S is obtained by estimating the volatilization of organic matter in the sludge, and after measuring the sludge organic matter content Corg, combining the temperature T and the humidity H using an empirical estimation formula to obtain the odor source estimation intensity S;

[0020] The estimated intensity S of the odor source is obtained by the following empirical estimation formula: ;

[0021] In the formula, β1, β2 and β3 represent empirical coefficients. Specifically, β1 represents the empirical coefficient of sludge organic matter content Corg, β2 represents the empirical coefficient of humidity H, and β3 represents the empirical coefficient of temperature T.

[0022] Preferably, S12, odor concentration information is collected in real time by odor sensors arranged at multiple locations of the treatment plant, and the average odor concentration Cobs of the treatment plant is obtained after processing the odor concentration information obtained at multiple locations, and the environmental information vector Fel is combined to form an environmental state feature vector F, and the timestamp information is marked for fixed period storage as historical data;

[0023] The environmental state feature vector F is specifically F={Fel, Cobs};

[0024] The odor sensor includes using a gas detection sensor to measure the odor concentration in the air, and obtaining the odor concentration Cobs (x, y) at the location (x, y) of the treatment plant.

[0025] Preferably, S2 includes S21 and S22;

[0026] S21, based on the acquired environmental state characteristic vector F and the principle of aerodynamics, establishing a dynamic odor diffusion prediction model, wherein the dynamic odor diffusion prediction model is established by using a Gaussian plume diffusion model to simulate the diffusion process of the odor in the air;

[0027] The dynamic odor diffusion prediction model established is used to simulate the diffusion of odor in the air, which is affected by wind speed W, wind direction θ and estimated odor source intensity S. The model-predicted odor concentration Cmodel (x, y, t) at the position (x, y) at different times t is calculated to obtain the model-predicted concentration set YCmodel at multiple monitoring points.

[0028] The predicted odor concentration Cmodel (x, y, t) is obtained by the following calculation formula: ;

[0029] In the formula, π is a mathematical constant with a value of 3.14159. and It represents the diffusion coefficient of odor, specifically the degree of diffusion in the horizontal y direction and the vertical z direction. exp represents the exponential function. G represents the pollutant release height, which is set by the geometric characteristics of the sludge tank.

[0030] Preferably, S22, by extracting the historically stored environmental state feature vector F, marking it as a historical odor concentration data set Hc, and performing short-term prediction with the environmental information vector Fel and the model predicted concentration set YCmodel, the short-term prediction is performed by using an autoregressive integral sliding average model, obtaining the predicted concentration Cpred(t+△t) after a period of time △t, and then dynamically correcting the predicted concentration Cpred(t+△t) in combination with the model predicted odor concentration Cmodel (x, y, t), obtaining the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and integrating multiple time points of future predictions to form an odor concentration prediction result vector CP;

[0031] The odor concentration prediction result vector CP is specifically CP={Cpred(x, y, t+1), Cpred(x, y, t+2), ..., Cpred(x, y, t+△t)}

[0032] The predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t is obtained by the following dynamic correction formula: ;

[0033] In the formula, λ represents the model weight coefficient, and its value range is 0<λ<1. represents the odor concentration at the position (x, y) at the current time t, m represents the maximum time step of the backtracking, γ(k) represents the weight coefficient of the backtracking time k, and Cobs(x, y, tk) represents the odor concentration at the position (x, y) at the backtracking time tk.

[0034] Preferably, said S3 includes S31;

[0035] S31, extracting historical additive dosing record data, marking it as historical main additive dosing record HD, and calculating it with the obtained odor concentration prediction result vector CP to obtain the simulated concentration change vector L and the additive dosing amount decision vector D;

[0036] The simulated concentration change vector L is obtained by the following calculation formula: ;

[0037] In the formula, L(t+△t) represents the odor concentration change vector at the future time t+△t, and △t represents the future time step;

[0038] The decision vector D is obtained by the following calculation formula: ;

[0039] Where D(t) represents the decision vector at the current time t, d1, d2 and d3 represent the adjustment coefficients. Specifically, d1 represents the adjustment coefficient of the simulated concentration change vector L(t+△t) at the future time t+△t, d2 represents the adjustment coefficient of the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and d3 represents the adjustment coefficient of the wind speed W.

[0040] Preferably, the S4 includes S41;

[0041] S41, after applying the decision vector D, the odor concentration state in the air will change, and simulation is performed based on the odor concentration prediction result vector CP and the decision vector D to obtain the adjusted concentration vector Cafter, the adjusted odor concentration change vector Gqs and the additive removal rate vector R;

[0042] The adjusted concentration vector Cafter is obtained by the following calculation formula: ;

[0043] Where Cafter(t+△t) represents the adjusted concentration vector at future time t+△t, and D(t+△t) represents the application decision vector at future time t+△t. It represents the additive removal efficiency coefficient;

[0044] The adjusted odor concentration change vector Gqs is obtained by the following calculation formula: ;

[0045] In the formula, Gqs (t+△t) represents the adjusted odor concentration change vector at future time t+△t;

[0046] The additive removal rate vector R is obtained by the following calculation formula: ;

[0047] Where R(t+△t) represents the additive removal rate vector at the future time t+△t.

[0048] Preferably, the S5 includes S51;

[0049] S51, based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, a comprehensive evaluation is performed to obtain the benefit index E, and the dosage of the additive is adjusted according to the benefit index E, and the execution decision vector D1 is obtained, and the sludge treatment process is adaptively adjusted according to the execution decision vector D1, and a boundary constraint is set for the execution decision vector D1;

[0050] Among them, the execution decision vector D1 adaptively regulates the sludge treatment process to achieve adaptive control by controlling the operation of the additive dosing pump and the dosing device;

[0051] The benefit index E is obtained by the following calculation formula: ;

[0052] In the formula, Ctarget represents the preset target odor concentration, e1, e2 and e3 represent the equity weight coefficients;

[0053] The execution decision vector D1 is obtained by the following calculation formula: ;

[0054] Where D1(t+△t) represents the execution decision vector at future time t+△t. Represents the step size factor, which is used to control the adjustment speed. It represents the gradient of benefit index to additive dosage, specifically, it means adjusting additive dosage to minimize benefit index E;

[0055] The boundary constraints are as follows: Dmin≤D1(t+△t)≤Dmax, where Dmin represents the minimum additive dosage and Dmax represents the maximum additive dosage.

[0056] Preferably, the S6 includes S61;

[0057] S61, recording the actual odor concentration Cfinal after executing the decision vector D1, the actual odor concentration Cfinal is obtained by extracting the odor concentration Cobs(x, y) at the position (x, y), and calculating the deviation △C from the preset target odor concentration, and comparing the deviation △C with the preset error threshold V to trigger the iterative optimization mechanism;

[0058] The deviation △C is The calculation formula is obtained, where △C (t) represents the deviation at the current time t;

[0059] The iterative optimization mechanism is triggered as follows:

[0060] When the deviation △C(t) at the current time t is greater than the error threshold V, it indicates that the iterative optimization mechanism is triggered and the additive dosing strategy adjustment is executed, including proportionally adjusting the execution decision vector D1 to execute the additive dosing strategy adjustment;

[0061] When the deviation △C(t) at the current time t ≤ the error threshold V, it means that the iterative optimization mechanism is not triggered.

[0062] A sludge treatment automation control system, comprising an information collection module, a trend analysis module, a trend decision module, a simulation prediction module, an optimization module and an iteration trigger module;

[0063] The information collection module collects environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F;

[0064] The trend analysis module constructs a dynamic odor diffusion prediction model based on the acquired environmental state feature vector F, calculates the odor concentration change trend at different time points, and obtains the odor concentration prediction result vector CP;

[0065] The trend decision module performs calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage;

[0066] The simulation prediction module simulates the change of odor concentration after applying the decision vector D, and obtains the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation;

[0067] The optimization module performs a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, the decision vector D and the odor concentration change vector Gqs, obtains the benefit index E, and adjusts the dosage of the additive according to the benefit index E, obtains the execution decision vector D1, and adaptively adjusts the sludge treatment process according to the execution decision vector D1;

[0068] The iterative trigger module records the actual odor concentration Cfinal after executing the decision vector D1, calculates the deviation △C from the preset target odor concentration, and triggers the iterative optimization mechanism according to the deviation △C.

[0069] The present invention provides a sludge treatment automation control system and method, which has the following beneficial effects:

[0070] (1) The environmental state feature vector F is collected in real time through the integrated sensor, and the odor concentration prediction result vector Cpred at different time points in the future is calculated based on the dynamic odor diffusion prediction model. Then, combined with the predicted concentration trend, the decision vector D of the additive dosage is dynamically adjusted. This method breaks through the limitation of the existing technology that only relies on real-time odor concentration feedback for dosage control. By combining the Gaussian plume diffusion model with the time series prediction method, the foresight and accuracy of the dosage decision are improved, and the dosage lag problem is effectively reduced. At the same time, by evaluating the benefit index E through the adjusted concentration vector Cafter and the odor concentration change vector Gafter, the dosage strategy is not only optimized based on the concentration change trend, but also fully considers factors such as the cost of additive use and the stability of odor diffusion, solving the defect that the dosage in the traditional method is difficult to balance the deodorization effect and cost.

[0071] (2) By extracting the historical main additive dosage record HD and combining it with the odor concentration prediction result vector Cpred, the simulated concentration change vector L and the additive dosage decision vector D at the future time t+△t are calculated, realizing the intelligent decision optimization of the additive dosage strategy. Compared with the traditional method that relies solely on real-time odor concentration feedback to add additives, this method can combine historical data with predicted trends to ensure that the dosage decision not only considers the future odor concentration change trend, but also dynamically adapts to environmental parameters such as wind speed W, thereby avoiding over- or under-dosage.

[0072] (3) By calculating the benefit index E based on the adjusted concentration vector Cafter, the decision vector D and the adjusted odor concentration change vector Gqs, the global optimization of the additive dosing strategy is achieved. Different from the traditional method that only focuses on a single indicator to adjust the dosage, this method quantifies the odor removal effect, the additive dosing cost, the stability of the odor concentration change and other factors through the benefit index E, and combines the boundary constraints of the execution decision vector D1, so that the additive dosage not only meets the odor concentration standard requirements, but also minimizes the use of additives and reduces resource waste. It can also adapt to different environmental conditions, effectively improve the long-term operating efficiency of the sludge treatment system, and ensure that the system always maintains the balance between optimal deodorization and energy saving and consumption reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a schematic diagram of a block diagram of a sludge treatment automation control method of the present invention;

[0074] Figure 2 The present invention is a schematic diagram of the steps of a sludge treatment automation control system. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] Example 1

[0077] The present invention provides a sludge treatment automation control method, please refer to Figure 1 , including the following steps:

[0078] S1, collect environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F;

[0079] S2. Based on the acquired environmental state feature vector F, a dynamic odor diffusion prediction model is constructed to calculate the odor concentration change trend at different time points and obtain the odor concentration prediction result vector CP;

[0080] S3, performing calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage;

[0081] S4, simulating the change of odor concentration after applying the decision vector D, and obtaining the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation;

[0082] S5. Perform a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, obtain the benefit index E, and adjust the dosage of the additive according to the benefit index E, obtain the execution decision vector D1, and adaptively adjust the sludge treatment process according to the execution decision vector D1;

[0083] S6. Record the actual odor concentration Cfinal after executing the decision vector D1, calculate the deviation △C from the preset target odor concentration, and trigger the iterative optimization mechanism according to the deviation △C.

[0084] In this embodiment, the environmental state feature vector F is collected in real time by an integrated sensor, and the odor concentration prediction result vector Cpred at different time points in the future is calculated based on the dynamic odor diffusion prediction model, and then the decision vector D of the amount of additives is dynamically adjusted in combination with the predicted concentration trend. This method breaks through the limitation of the prior art that only relies on real-time odor concentration feedback for dosing control. By combining the Gaussian plume diffusion model with the time series prediction method, the foresight and accuracy of the dosing decision are improved, and the dosing lag problem is effectively reduced. At the same time, the benefit index E is evaluated by adjusting the concentration vector Cafter and the odor concentration change vector Gafter, so that the dosing strategy is not only optimized based on the concentration change trend, but also fully considers factors such as the cost of additive use and the stability of odor diffusion, solving the defect that the dosage in the traditional method is difficult to balance the deodorization effect and cost. In addition, by real-time recording the actual odor concentration Cfinal after executing the decision vector D1 and calculating the odor concentration deviation vector △C, an iterative optimization mechanism is formed, so that the additive addition can dynamically adapt to different environmental changes, achieve precise control, and avoid the problem of excessive or insufficient dosage caused by environmental condition fluctuations in the traditional method. Therefore, this method significantly improves the intelligence level of the sludge treatment system, the stability of the deodorization effect and the utilization efficiency of the additives, realizes the comprehensive optimization of automation, predictive control and energy saving and consumption reduction, and greatly improves the environmental protection and economy of the sludge treatment process.

[0085] Example 2

[0086] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0087] S11, collecting environmental information that affects odor diffusion during sludge treatment in real time through integrated multiple sensors, and forming an environmental information vector Fel, wherein the environmental information vector Fel includes temperature T, humidity H, wind speed W, wind direction θ, air pressure P and odor source estimated intensity S;

[0088] Among them, the sludge treatment process includes sludge storage process, conditioning process, dehydration process and stacking process;

[0089] Among them, the various sensors include temperature sensors, humidity sensors, ultrasonic anemometers, wind direction sensors, and air pressure sensors;

[0090] The odor source estimation intensity S is obtained by estimating the volatilization of organic matter in the sludge, and after measuring the sludge organic matter content Corg, combining the temperature T and the humidity H using an empirical estimation formula to obtain the odor source estimation intensity S;

[0091] The estimated intensity S of the odor source is obtained by the following empirical estimation formula: ;

[0092] In the formula, β1, β2 and β3 represent empirical coefficients. Specifically, β1 represents the empirical coefficient of sludge organic matter content Corg, β2 represents the empirical coefficient of humidity H, and β3 represents the empirical coefficient of temperature T.

[0093] S12, collect odor concentration information in real time by odor sensors installed at multiple locations in the treatment plant, obtain the average odor concentration Cobs of the treatment plant after processing the odor concentration information obtained at multiple locations, and combine it with the environmental information vector Fel to form an environmental state feature vector F, and mark the timestamp information for fixed-period storage as historical data;

[0094] The environmental state feature vector F is specifically F={Fel, Cobs};

[0095] The odor sensor includes using a gas detection sensor to measure the odor concentration in the air, and obtaining the odor concentration Cobs (x, y) at the location (x, y) of the treatment plant.

[0096] In this embodiment, environmental information that affects odor diffusion is collected in real time through the integration of multiple sensors to form an environmental information vector Fel, and odor concentration information Cobs is obtained in combination with odor sensors deployed at multiple points, and finally an environmental state feature vector F containing a timestamp is constructed for fixed-period storage. Compared with the mode of sampling at a single time point or periodic detection in traditional sludge treatment processes, this method can continuously and in real time monitor environmental changes. The odor source estimated intensity S calculated by the empirical formula can not only accurately reflect the volatilization of organic matter in the sludge, but also provide supplementary data when the actual monitoring data is insufficient or the sensor is abnormal, thereby avoiding the problem of misjudgment of odor concentration caused by abnormal data from a single sensor. Combined with the spatial layout strategy of multiple odor sensors, more stable input data is provided for the subsequent dynamic odor diffusion prediction model, which improves the robustness and environmental adaptability of the prediction.

[0097] Example 3

[0098] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0099] S21, based on the acquired environmental state characteristic vector F and the principle of aerodynamics, establishing a dynamic odor diffusion prediction model, wherein the dynamic odor diffusion prediction model is established by using a Gaussian plume diffusion model to simulate the diffusion process of the odor in the air;

[0100] The dynamic odor diffusion prediction model established is used to simulate the diffusion of odor in the air, which is affected by wind speed W, wind direction θ and estimated odor source intensity S. The model-predicted odor concentration Cmodel (x, y, t) at the position (x, y) at different times t is calculated to obtain the model-predicted concentration set YCmodel at multiple monitoring points.

[0101] The predicted odor concentration Cmodel (x, y, t) is obtained by the following calculation formula: ;

[0102] In the formula, π is a mathematical constant with a value of 3.14159. and It represents the diffusion coefficient of the odor, specifically the degree of diffusion in the horizontal y direction and the vertical z direction, exp represents the exponential function, G represents the pollutant release height, which is set by the geometric characteristics of the sludge tank; It is used to calculate the basic concentration of odor, and the ratio of the intensity S estimated by the odor source and the wind speed W determines the effect of wind speed on odor transmission. and It represents the diffusion range of the odor. The larger the diffusion range, the lower the odor concentration per unit volume. Indicates the effect of calculating the diffusion in the horizontal y direction. y² represents the offset from the horizontal y axis. The farther away from the y axis, the lower the concentration. Indicates the calculation of the influence in the vertical z direction, It represents the offset between the height z of a certain point and the height G of the pollution source. The farther away from the height G, the lower the concentration.

[0103] S22, extract the historically stored environmental state feature vector F, mark it as the historical odor concentration data set Hc, and perform short-term prediction with the environmental information vector Fel and the model predicted concentration set YCmodel, the short-term prediction is performed by using the autoregressive integral sliding average model, obtain the predicted concentration Cpred(t+△t) after a period of time △t, and then dynamically correct the predicted concentration Cpred(t+△t) in combination with the model predicted odor concentration Cmodel (x, y, t), obtain the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and integrate multiple time points of future prediction to form the odor concentration prediction result vector CP;

[0104] The odor concentration prediction result vector CP is specifically CP={Cpred(x, y, t+1), Cpred(x, y, t+2), ..., Cpred(x, y, t+△t)}

[0105] The predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t is obtained by the following dynamic correction formula: ;

[0106] In the formula, λ represents the model weight coefficient, which ranges from 0<λ<1 and is specifically used to control the influence of the model on predicting the odor concentration Cmodel (x, y, t). represents the odor concentration at the position (x, y) at the current time t, m represents the maximum time step of the backtracking, γ(k) represents the weight coefficient of the backtracking time k, which is specifically used to adjust the influence of different historical data, Cobs(x, y, tk) represents the odor concentration at the position (x, y) at the backtracking time tk, It means analyzing the impact of historical observation data on future predictions;

[0107] In this embodiment, the dynamic odor diffusion prediction model based on the environmental state characteristic vector F and the aerodynamic principle is used to accurately simulate the diffusion process of odor in the air, and the Gaussian plume diffusion model is used to calculate the model prediction concentration set YCmodel at different time points and different spatial positions, which significantly improves the understanding and prediction accuracy of odor diffusion behavior. Compared with the traditional prediction method that only relies on single-point monitoring and static data modeling, the calculation of the predicted concentration Cpred(x, y, t+Δt) at a specific time t+Δt and a specific position (x, y) in the future is realized. At the same time, by extracting the historical environmental state characteristic vector F to form a historical odor concentration data set HC, and combining the autoregressive integral sliding average model, this method not only predicts odor diffusion based on a mathematical model, but also dynamically integrates historical monitoring data to form an odor concentration prediction result vector Cpred, avoiding the problem of accumulated prediction errors caused by fluctuations in environmental factors in traditional methods. In addition, the model prediction concentration Cmodel(x, y, t) is adjusted by a dynamic correction formula so that the predicted value can better match the actual situation, breaking through the problem that the odor concentration prediction in the prior art cannot adapt to different working conditions. Therefore, this method not only significantly improves the prediction accuracy of odor concentration changes during sludge treatment, but also maintains strong adaptability and robustness under complex environmental conditions, providing more scientific data support for the optimization of additive dosing, thereby achieving more accurate intelligent control of sludge treatment.

[0108] Example 4

[0109] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S3 includes S31;

[0110] S31, extracting historical additive dosing record data, marking it as historical main additive dosing record HD, and calculating it with the obtained odor concentration prediction result vector CP to obtain the simulated concentration change vector L and the additive dosing amount decision vector D;

[0111] The simulated concentration change vector L is obtained by the following calculation formula: ;

[0112] In the formula, L(t+△t) represents the odor concentration change vector at the future time t+△t, and △t represents the future time step;

[0113] The decision vector D is obtained by the following calculation formula: ;

[0114] Where D(t) represents the decision vector at the current time t, d1, d2 and d3 represent the adjustment coefficients. Specifically, d1 represents the adjustment coefficient of the simulated concentration change vector L(t+△t) at the future time t+△t, d2 represents the adjustment coefficient of the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and d3 represents the adjustment coefficient of the wind speed W.

[0115] The S4 includes S41;

[0116] S41, after applying the decision vector D, the odor concentration state in the air will change, and simulation is performed based on the odor concentration prediction result vector CP and the decision vector D to obtain the adjusted concentration vector Cafter, the adjusted odor concentration change vector Gqs and the additive removal rate vector R;

[0117] The adjusted concentration vector Cafter is obtained by the following calculation formula: ;

[0118] Where Cafter(t+△t) represents the adjusted concentration vector at future time t+△t, and D(t+△t) represents the application decision vector at future time t+△t. It indicates the removal efficiency coefficient of the additive, specifically indicating the odor concentration that can be reduced by 1L of additive. It is also used to describe the effect of the additive on the pollutants in the odor. Its function is to measure how much odor concentration can be removed by a unit volume of additive.

[0119] The adjusted odor concentration change vector Gqs is obtained by the following calculation formula: ;

[0120] In the formula, Gqs (t+△t) represents the adjusted odor concentration change vector at future time t+△t;

[0121] The additive removal rate vector R is obtained by the following calculation formula: ;

[0122] Where R(t+△t) represents the additive removal rate vector at the future time t+△t.

[0123] In this embodiment, by extracting the historical main auxiliary agent addition record HD and combining it with the odor concentration prediction result vector Cpred, the simulated concentration change vector L and the decision vector D of the auxiliary agent addition amount at the future time t+△t are calculated, and the intelligent decision optimization of the auxiliary agent addition strategy is realized. Compared with the traditional method that relies solely on real-time odor concentration feedback to add auxiliary agents, this method can combine historical data with predicted trends to ensure that the addition decision not only considers the future odor concentration change trend, but also dynamically adapts to environmental parameters such as wind speed W, thereby avoiding excessive or insufficient addition. Furthermore, by simulating the change in the odor concentration state in the air after applying the decision vector D, this method can obtain the adjusted concentration vector Cafter, the adjusted odor concentration change vector Gqs and the auxiliary agent removal rate vector R, providing a quantitative basis for the effect evaluation after the addition of the auxiliary agent. In particular, the introduction of the auxiliary agent removal rate vector R makes the auxiliary agent addition strategy not only focus on the degree of reduction in odor concentration, but also measure the deodorization efficiency of the unit auxiliary agent, thereby optimizing the use of auxiliary agents and avoiding waste of resources. In addition, by calculating the future odor diffusion trend through the adjusted odor concentration change vector Gqs, the system can predictively adjust the dosing strategy, breaking through the limitation of the traditional method that can only rely on passive feedback to adjust the dosage. Therefore, this method not only realizes intelligent dosing decision-making based on prediction, but also can dynamically balance between the utilization rate of additives, deodorization effect and environmental adaptability, making the automated control of the sludge treatment system more accurate, efficient and energy-saving.

[0124] Example 5

[0125] This embodiment is explained in Example 4. Please refer to Figure 1 Specifically: the S5 includes S51;

[0126] S51, based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, a comprehensive evaluation is performed to obtain the benefit index E, and the dosage of the additive is adjusted according to the benefit index E, and the execution decision vector D1 is obtained, and the sludge treatment process is adaptively adjusted according to the execution decision vector D1, and a boundary constraint is set for the execution decision vector D1;

[0127] Among them, the execution decision vector D1 adaptively regulates the sludge treatment process to achieve adaptive control by controlling the operation of the additive dosing pump and the dosing device; at the same time, during the sludge conditioning stage, the additive and sludge are fully mixed by controlling the intensity and time of the stirring device to improve the deodorization efficiency;

[0128] The benefit index E is obtained by the following calculation formula: ;

[0129] In the formula, Ctarget represents the preset target odor concentration, e1, e2 and e3 represent equity weight coefficients, specifically e1 controls the impact of odor removal effect on benefits, e2 controls the impact of additive dosage on benefits, and e3 controls the stability of odor concentration changes;

[0130] By obtaining the benefit index E, the odor concentration can be accurately controlled to make it close to the target odor concentration Ctarget. At the same time, by calculating the deviation between the current odor concentration Cafete and the target odor concentration Ctarget, it is ensured that the dosing strategy can effectively reduce the odor concentration and prevent excessive deodorization from causing waste of resources.

[0131] Application scenarios include:

[0132] During the sludge conditioning stage (adjusting the dosage of additives and optimizing mixing efficiency):

[0133] In the sludge conditioning tank, the current additive mixing effect is evaluated by calculating the benefit index E to ensure that odor control is effective and additives are not wasted, and then the stirring time or additive concentration is adjusted;

[0134] Sludge dewatering stage (optimizing the ratio of additives and dehydrating agents):

[0135] By calculating the benefit index E, the dosage of additives is controlled to ensure that the release of volatile gases from the dehydrated sludge is minimized, while reducing the waste of chemicals caused by excessive dosage;

[0136] The execution decision vector D1 is obtained by the following calculation formula: ;

[0137] Where D1(t+△t) represents the execution decision vector at future time t+△t. Represents the step size factor, which is used to control the adjustment speed. It represents the gradient of benefit index to additive dosage, specifically, it means adjusting additive dosage to minimize benefit index E;

[0138] The boundary constraints are as follows:

[0139] Dmin≤D1(t+△t)≤Dmax, where Dmin represents the minimum dosage of additives, which is used to prevent insufficient dosage, and Dmax represents the maximum dosage of additives, which is used to prevent waste caused by excessive dosage.

[0140] The S6 includes S61;

[0141] S61, recording the actual odor concentration Cfinal after executing the decision vector D1, the actual odor concentration Cfinal is obtained by extracting the odor concentration Cobs(x, y) at the position (x, y), and calculating the deviation △C from the preset target odor concentration, and comparing the deviation △C with the preset error threshold V to trigger the iterative optimization mechanism;

[0142] The deviation △C is The calculation formula is obtained, where △C (t) represents the deviation at the current time t;

[0143] The iterative optimization mechanism is triggered as follows:

[0144] When the deviation △C(t) at the current time t is greater than the error threshold V, it indicates that the iterative optimization mechanism is triggered and the additive dosing strategy adjustment is executed, including proportionally adjusting the execution decision vector D1 to execute the additive dosing strategy adjustment;

[0145] When the deviation △C(t) at the current time t ≤ the error threshold V, it means that the iterative optimization mechanism is not triggered.

[0146] In this embodiment, the global optimization of the additive dosing strategy is achieved by calculating the benefit index E based on the adjusted concentration vector Cafter, the decision vector D and the adjusted odor concentration change vector Gqs. Different from the traditional method that only focuses on a single indicator to adjust the dosage, this method quantifies the odor removal effect, the additive dosage cost, the stability of the odor concentration change and other factors through the benefit index E, and combines the boundary constraints of the execution decision vector D1, so that the additive dosage not only meets the odor concentration compliance requirements, but also minimizes the amount of additives used, reducing resource waste. At the same time, this method calculates the actual odor concentration Cfinal after executing the decision vector D1, and calculates the odor concentration deviation △C based on the preset target odor concentration Ctarget, so that the system can continuously monitor the effectiveness of the dosing decision. In particular, the error threshold V is introduced as the trigger condition of the iterative optimization mechanism to ensure that adjustments are made only when the deviation of the dosing strategy exceeds the acceptable range, to prevent frequent adjustments from causing system instability or erroneous dosing. In addition, the optimized additive dosing strategy adopts proportional adjustment to execute the decision vector D1, making the dosing optimization process more stable and avoiding the system fluctuation caused by sudden adjustment in the traditional control strategy. Therefore, this method can not only dynamically optimize the additive dosing strategy and improve the accuracy and stability of dosing decisions, but also adapt to different environmental conditions, effectively improve the long-term operation efficiency of the sludge treatment system, and ensure that the system always maintains the optimal balance between deodorization and energy saving and consumption reduction.

[0147] Example 6

[0148] A sludge treatment automation control system, please refer to Figure 2,Specifically: including information acquisition module, trend analysis module, trend decision module, simulation prediction module, optimization module and iteration trigger module;

[0149] The information collection module collects environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F;

[0150] The trend analysis module constructs a dynamic odor diffusion prediction model based on the acquired environmental state feature vector F, calculates the odor concentration change trend at different time points, and obtains the odor concentration prediction result vector CP;

[0151] The trend decision module performs calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage;

[0152] The simulation prediction module simulates the change of odor concentration after applying the decision vector D, and obtains the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation;

[0153] The optimization module performs a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, the decision vector D and the odor concentration change vector Gqs, obtains the benefit index E, and adjusts the dosage of the additive according to the benefit index E, obtains the execution decision vector D1, and adaptively adjusts the sludge treatment process according to the execution decision vector D1;

[0154] The iterative trigger module records the actual odor concentration Cfinal after executing the decision vector D1, calculates the deviation △C from the preset target odor concentration, and triggers the iterative optimization mechanism according to the deviation △C.

[0155] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sludge treatment automation control method, characterized in that: The following steps are involved: S1, collect environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F; S2. Based on the acquired environmental state feature vector F, a dynamic odor diffusion prediction model is constructed to calculate the odor concentration change trend at different time points and obtain the odor concentration prediction result vector CP; S3, performing calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage; S4, simulating the change of odor concentration after applying the decision vector D, and obtaining the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation; S5. Perform a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, obtain the benefit index E, and adjust the dosage of the additive according to the benefit index E, obtain the execution decision vector D1, and adaptively adjust the sludge treatment process according to the execution decision vector D1; S6. Record the actual odor concentration Cfinal after executing the decision vector D1, calculate the deviation △C from the preset target odor concentration, and trigger the iterative optimization mechanism according to the deviation △C.

2. The sludge treatment automation control method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, collecting environmental information that affects odor diffusion during sludge treatment in real time through integrated multiple sensors, and forming an environmental information vector Fel, wherein the environmental information vector Fel includes temperature T, humidity H, wind speed W, wind direction θ, air pressure P and odor source estimated intensity S; Among them, the sludge treatment process includes sludge storage process, conditioning process, dehydration process and stacking process; Among them, the various sensors include temperature sensors, humidity sensors, ultrasonic anemometers, wind direction sensors, and air pressure sensors; The odor source estimation intensity S is obtained by estimating the volatilization of organic matter in the sludge, and after measuring the sludge organic matter content Corg, combining the temperature T and the humidity H using an empirical estimation formula to obtain the odor source estimation intensity S; The estimated intensity S of the odor source is obtained by the following empirical estimation formula: ； In the formula, β1, β2 and β3 represent empirical coefficients. Specifically, β1 represents the empirical coefficient of sludge organic matter content Corg, β2 represents the empirical coefficient of humidity H, and β3 represents the empirical coefficient of temperature T.

3. The sludge treatment automation control method according to claim 2 is characterized in that: S12, collect odor concentration information in real time by odor sensors installed at multiple locations in the treatment plant, obtain the average odor concentration Cobs of the treatment plant after processing the odor concentration information obtained at multiple locations, and combine it with the environmental information vector Fel to form an environmental state feature vector F, and mark the timestamp information for fixed-period storage as historical data; The environmental state feature vector F is specifically F={Fel, Cobs}; The odor sensor includes using a gas detection sensor to measure the odor concentration in the air, and obtaining the odor concentration Cobs (x, y) at the location (x, y) of the treatment plant.

4. The sludge treatment automation control method according to claim 3 is characterized in that: The S2 includes S21 and S22; S21, based on the acquired environmental state characteristic vector F and the principle of aerodynamics, establishing a dynamic odor diffusion prediction model, wherein the dynamic odor diffusion prediction model is established by using a Gaussian plume diffusion model to simulate the diffusion process of the odor in the air; The dynamic odor diffusion prediction model established is used to simulate the diffusion of odor in the air, which is affected by wind speed W, wind direction θ and estimated odor source intensity S. The model-predicted odor concentration Cmodel (x, y, t) at the position (x, y) at different times t is calculated to obtain the model-predicted concentration set YCmodel at multiple monitoring points. The predicted odor concentration Cmodel (x, y, t) is obtained by the following calculation formula: ； In the formula, π is a mathematical constant with a value of 3.14159. and It represents the diffusion coefficient of odor, specifically the degree of diffusion in the horizontal y direction and the vertical z direction. exp represents the exponential function. G represents the pollutant release height, which is set by the geometric characteristics of the sludge tank.

5. A sludge treatment automation control method according to claim 4, characterized in that: S22, extract the historically stored environmental state feature vector F, mark it as the historical odor concentration data set Hc, and perform short-term prediction with the environmental information vector Fel and the model predicted concentration set YCmodel, the short-term prediction is performed by using the autoregressive integral sliding average model, obtain the predicted concentration Cpred(t+△t) after a period of time △t, and then dynamically correct the predicted concentration Cpred(t+△t) in combination with the model predicted odor concentration Cmodel (x, y, t), obtain the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and integrate multiple time points of future prediction to form the odor concentration prediction result vector CP; The odor concentration prediction result vector CP is specifically CP={Cpred(x, y, t+1), Cpred(x, y, t+2), ..., Cpred(x, y, t+△t)} The predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t is obtained by the following dynamic correction formula: ； In the formula, λ represents the model weight coefficient, and its value range is 0<λ<1. represents the odor concentration at the position (x, y) at the current time t, m represents the maximum time step of the backtracking, γ(k) represents the weight coefficient of the backtracking time k, and Cobs(x, y, tk) represents the odor concentration at the position (x, y) at the backtracking time tk.

6. A sludge treatment automation control method according to claim 5, characterized in that: The S3 includes S31; S31, extracting historical additive dosing record data, marking it as historical main additive dosing record HD, and calculating it with the obtained odor concentration prediction result vector CP to obtain the simulated concentration change vector L and the additive dosing amount decision vector D; The simulated concentration change vector L is obtained by the following calculation formula: ； In the formula, L(t+△t) represents the odor concentration change vector at the future time t+△t, and △t represents the future time step; The decision vector D is obtained by the following calculation formula: ； Where D(t) represents the decision vector at the current time t, d1, d2 and d3 represent the adjustment coefficients. Specifically, d1 represents the adjustment coefficient of the simulated concentration change vector L(t+△t) at the future time t+△t, d2 represents the adjustment coefficient of the predicted concentration Cpred(x, y, t+△t) at the position (x, y) at the future time t+△t, and d3 represents the adjustment coefficient of the wind speed W.

7. The sludge treatment automation control method according to claim 6, characterized in that: The S4 includes S41; S41, after applying the decision vector D, the odor concentration state in the air will change, and simulation is performed based on the odor concentration prediction result vector CP and the decision vector D to obtain the adjusted concentration vector Cafter, the adjusted odor concentration change vector Gqs and the additive removal rate vector R; The adjusted concentration vector Cafter is obtained by the following calculation formula: ; Where Cafter(t+△t) represents the adjusted concentration vector at future time t+△t, and D(t+△t) represents the decision vector at future time t+△t. It represents the additive removal efficiency coefficient; The adjusted odor concentration change vector Gqs is obtained by the following calculation formula: ； In the formula, Gqs (t+△t) represents the adjusted odor concentration change vector at future time t+△t; The additive removal rate vector R is obtained by the following calculation formula: ； Where R(t+△t) represents the additive removal rate vector at the future time t+△t.

8. The sludge treatment automation control method according to claim 7, characterized in that: The S5 includes S51; S51, based on the obtained adjusted concentration vector Cafter, decision vector D and odor concentration change vector Gqs, a comprehensive evaluation is performed to obtain the benefit index E, and the dosage of the additive is adjusted according to the benefit index E, and the execution decision vector D1 is obtained, and the sludge treatment process is adaptively adjusted according to the execution decision vector D1, and a boundary constraint is set for the execution decision vector D1; Among them, the execution decision vector D1 adaptively regulates the sludge treatment process to achieve adaptive control by controlling the operation of the additive dosing pump and the dosing device; The benefit index E is obtained by the following calculation formula: ； In the formula, Ctarget represents the preset target odor concentration, e1, e2 and e3 represent the equity weight coefficients; The execution decision vector D1 is obtained by the following calculation formula: ； Where D1(t+△t) represents the execution decision vector at future time t+△t. Represents the step size factor, which is used to control the adjustment speed. It represents the gradient of benefit index to additive dosage, specifically, it means adjusting additive dosage to minimize benefit index E; The boundary constraints are as follows: Dmin≤D1(t+△t)≤Dmax, where Dmin represents the minimum additive dosage and Dmax represents the maximum additive dosage.

9. The sludge treatment automation control method according to claim 8, characterized in that: The S6 includes S61; S61, recording the actual odor concentration Cfinal after executing the decision vector D1, the actual odor concentration Cfinal is obtained by extracting the odor concentration Cobs(x, y) at the position (x, y), and calculating the deviation △C from the preset target odor concentration, and comparing the deviation △C with the preset error threshold V to trigger the iterative optimization mechanism; The deviation △C is The calculation formula is obtained, where △C (t) represents the deviation at the current time t; The iterative optimization mechanism is triggered as follows: When the deviation △C(t) at the current time t is greater than the error threshold V, it indicates that the iterative optimization mechanism is triggered and the additive dosing strategy adjustment is executed, including proportionally adjusting the execution decision vector D1 to execute the additive dosing strategy adjustment; When the deviation △C(t) at the current time t ≤ the error threshold V, it means that the iterative optimization mechanism is not triggered.

10. A sludge treatment automation control system, applied to a sludge treatment automation control method according to any one of claims 1 to 9, characterized in that: It includes information collection module, trend analysis module, trend decision module, simulation prediction module, optimization module and iteration trigger module; The information collection module collects environmental information and odor concentration information in the sludge treatment process in real time through integrated sensors to form an environmental state feature vector F; The trend analysis module constructs a dynamic odor diffusion prediction model based on the acquired environmental state feature vector F, calculates the odor concentration change trend at different time points, and obtains the odor concentration prediction result vector CP; The trend decision module performs calculation based on the obtained odor concentration prediction result vector CP to obtain the decision vector D of the additive dosage; The simulation prediction module simulates the change of odor concentration after applying the decision vector D, and obtains the odor concentration change vector Gqs and the adjusted concentration vector Cafter calculated by the odor concentration simulation; The optimization module performs a comprehensive evaluation based on the obtained adjusted concentration vector Cafter, the decision vector D and the odor concentration change vector Gqs, obtains the benefit index E, and adjusts the dosage of the additive according to the benefit index E, obtains the execution decision vector D1, and adaptively adjusts the sludge treatment process according to the execution decision vector D1; The iterative trigger module records the actual odor concentration Cfinal after executing the decision vector D1, calculates the deviation △C from the preset target odor concentration, and triggers the iterative optimization mechanism according to the deviation △C.

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