Environment-friendly treatment method and equipment for drying tail gas of oil-containing sludge drying

Abstract

This application relates to the field of exhaust gas treatment technology, specifically to a method and equipment for environmentally friendly treatment of dried exhaust gas from oily sludge drying. The method includes: introducing the dried exhaust gas from the oily sludge drying process into a desulfurization spray tower; real-time acquisition of the gas temperature and sulfur concentration at the outlet after spraying, as well as the actual pH value of the spray liquid at each moment; calculating the adsorption coefficient and corrected development coefficient at each moment; predicting the sulfur concentration at the next moment using a grey prediction model; determining the target pH value corresponding to the next moment; obtaining the control parameters of a PID control algorithm using a neural network model; and using the PID control algorithm to real-time regulate the pH value of the spray liquid in the desulfurization spray tower. This application can improve the precise control of the pH value of the spray liquid in the desulfurization spray tower, thereby improving the desorption effect and efficiency of the dried exhaust gas.

Description

Technical Field

[0001] This application relates to the field of exhaust gas treatment technology, specifically to a method and equipment for environmentally friendly treatment of dried exhaust gas from oily sludge drying. Background Technology

[0002] Oily sludge contains a large amount of petroleum-based substances, heavy metals, and harmful substances. Drying oily sludge will generate a large amount of drying exhaust gas, which contains high concentrations of sulfides. Direct discharge of this gas will cause great pollution to the environment and threaten the ecological environment and human health. Therefore, it is necessary to desulfurize the drying exhaust gas.

[0003] Since the drying temperature affects the amount of sulfides emitted in the dried tail gas, and the absorption efficiency of sulfides is closely related to the pH value of the spray solution, when the dried tail gas is sprayed and adsorbed through the desulfurization spray tower, the concentration of sulfides in the tail gas changes due to the change in drying temperature. The spray solution with a fixed pH value cannot adapt to this change, resulting in unstable absorption efficiency of the spray solution. At the same time, the traditional PID control algorithm cannot adjust the pH value of the spray solution according to the real-time change of sulfur concentration. The traditional PID control algorithm lacks the ability to sense and respond to real-time changes in sulfur concentration and cannot accurately adjust the pH value of the spray solution, resulting in unsatisfactory treatment effect on the dried tail gas. Summary of the Invention

[0004] To address the aforementioned technical issues, a method and equipment for the environmentally friendly treatment of drying exhaust gas from oily sludge drying are provided to resolve the existing problems.

[0005] The solution to the technical problem in this application is to provide a method and equipment for the environmentally friendly treatment of drying exhaust gas from oily sludge drying, including the following steps:

[0006] In a first aspect, embodiments of this application provide an environmentally friendly treatment method for the exhaust gas from the drying of oily sludge, the method comprising the following steps:

[0007] During the drying process of oily sludge, the drying tail gas is introduced into the desulfurization spray tower, and the gas temperature and sulfur concentration at the outlet after spraying, as well as the actual pH value of the spray liquid at each time, are collected in real time.

[0008] Analyze the deviations in gas temperature and sulfur concentration at different times, and calculate the adsorption coefficient at each time.

[0009] By analyzing the correlation between gas temperature and sulfur concentration at different times before each time step, as well as the magnitude of the adsorption coefficient, the development coefficient of the grey prediction model is corrected to obtain the corrected development coefficient for each time step. The grey prediction model is then used to predict the sulfur concentration to obtain the predicted concentration for the next time step.

[0010] Based on the rate of change of sulfur concentration at each time point and the predicted concentration at the next time point, the target pH value at the next time point is determined.

[0011] Based on the deviation between the actual pH value at each time point and the target pH value at the next time point, the deviation is calculated. A neural network model is used to obtain the control parameters of the PID control algorithm. Combining the deviation at each time point and the change of the deviation, the pH value of the spray liquid in the desulfurization spray tower is adjusted in real time using the PID control algorithm.

[0012] Preferably, the calculation of the adsorption coefficient at each time point includes:

[0013] The ratio of the gas temperature at each moment to the preset spray liquid temperature is recorded as a relative ratio.

[0014] The difference between the preset critical value and the sulfur concentration at each time point is calculated and denoted as the relative difference.

[0015] The relative ratio and the relative difference are combined to determine the adsorption coefficient at each time point.

[0016] Preferably, the specific process of the fusion is as follows: the product of the relative ratio and the relative difference is used as the adsorption coefficient at each time point.

[0017] Preferably, obtaining the corrected development coefficients for each time point includes:

[0018] Each moment and the multiple moments preceding it are recorded as a local time period of each moment;

[0019] Calculate the correlation between gas temperature and sulfur concentration at all times within the local time period;

[0020] The corresponding corrected development coefficient at each moment The calculation formula is: ,in, for The degree of relevance of time, for The adsorption coefficient at time t. This is the preset initial development coefficient.

[0021] Preferably, obtaining the predicted concentration for the next moment includes: using a grey prediction model to obtain the predicted concentration for the next moment based on the sulfur concentration at all times within the local time period of each moment and the corrected development coefficient.

[0022] Preferably, determining the target pH value at the next moment includes: ,in, for The target pH value at that time. The preset initial pH value, for Predicted concentration at time, for The sulfur concentration at any given time, This is the normalization function.

[0023] Preferably, the deviation is the difference between the actual pH value at each time point and the target pH value at the next time point.

[0024] Preferably, the step of obtaining the control parameters of the PID control algorithm includes: tuning the control parameters of the PID control algorithm using a BP neural network model based on the actual pH value at each time, the target pH value of the next time corresponding to each time, and the deviation, thereby obtaining the control parameters of the PID algorithm.

[0025] Preferably, the real-time control of the pH value of the spray liquid in the desulfurization spray tower includes: recording the difference between the deviation amount at each time and the previous time as the deviation change rate; and using the deviation amount and the deviation change rate as inputs to the PID control algorithm based on the control parameters of the PID control algorithm to control the pH value of the spray liquid at the next time.

[0026] Secondly, embodiments of this application also provide an environmentally friendly treatment device for the drying exhaust gas of oily sludge drying, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described environmentally friendly treatment methods for the drying exhaust gas of oily sludge drying.

[0027] This application has at least the following beneficial effects:

[0028] This application analyzes the deviations in gas temperature and sulfur concentration at various times to calculate the adsorption coefficient at each time point. Its beneficial effect lies in considering the changes in gas temperature and sulfur concentration, reflecting the adsorption and capture capacity of the spray liquid on the exhaust gas. The development coefficient of the grey prediction model is corrected to obtain the corrected development coefficient for each time point. The grey prediction model is then used to predict the sulfur concentration at the next time point. Its beneficial effect lies in considering the relationship between gas temperature and sulfur concentration, as well as the saturation of the spray liquid's absorption of the exhaust gas, dynamically adjusting the development coefficient of the grey prediction model, and improving the accuracy of sulfur concentration prediction. Finally, the target pH value for the next time point is determined. Its beneficial effect lies in using the changes in sulfur concentration in the exhaust gas... In this case, a target pH value is reasonably set to ensure that the pH value of the spray liquid adapts to changes in sulfur concentration. Based on the deviation between the actual pH value at each time and the target pH value at the next time, the deviation is calculated. The control parameters of the PID control algorithm are obtained using a neural network model. Combined with the deviation at each time and the change of the deviation, the pH value of the spray liquid in the desulfurization spray tower is adjusted in real time using the PID control algorithm. The beneficial effect is that by dynamically adjusting the control parameters of the PID control algorithm, it can respond promptly to the fluctuation of sulfur concentration in the tail gas, improve the precision of pH control of the spray liquid in the desulfurization spray tower, improve the desorption effect and desorption efficiency of the dried tail gas, and ensure that the sulfur emission concentration in the tail gas is always lower than the environmental protection standard limit. Attached Figure Description

[0029] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of the environmentally friendly treatment method for the exhaust gas from the drying of oily sludge.

[0030] Figure 1 A flowchart illustrating the steps of the method for environmentally friendly treatment of drying exhaust gas from oily sludge drying provided in this application embodiment;

[0031] Figure 2 A schematic diagram of a desulfurization spray tower provided in an embodiment of this application;

[0032] Figure 3 A flowchart illustrating the steps of obtaining control parameters for the PID control algorithm provided in this application embodiment. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description, in conjunction with the accompanying drawings and embodiments, provides a more comprehensive explanation of the environmentally friendly treatment method and equipment for the drying exhaust gas of oily sludge. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0035] Please see Figure 1 The diagram illustrates a flowchart of a method for environmentally friendly treatment of drying exhaust gas from oily sludge drying, according to an embodiment of this application. The method includes the following steps:

[0036] Step 1: During the drying process of oily sludge, the drying tail gas is introduced into the desulfurization spray tower. The gas temperature and sulfur concentration at the outlet after spraying, as well as the actual pH value of the spray liquid at each time, are collected in real time.

[0037] The production of petrochemical products generates a large amount of oily solid waste, namely oily sludge. This sludge is rich in petroleum hydrocarbons and contains numerous toxic and harmful substances, causing significant damage to the ecological environment. Therefore, the treatment of oily sludge generally employs sludge drying technology. First, the oily sludge is dehydrated using a centrifuge to reduce its moisture content. It is then transported to a drying kiln for high-temperature drying. After drying, the sludge is conveyed by a screw conveyor and, along with other organic matter, enters an incinerator for incineration.

[0038] Secondly, both drying and incineration processes generate large amounts of exhaust gas, which contains a significant amount of sulfur-containing acidic gases. Direct discharge of this gas would pollute the environment. Therefore, a desulfurization spray tower is needed to desulfurize and adsorb the exhaust gas. A schematic diagram of the desulfurization spray tower provided in this embodiment is shown below. Figure 2 As shown, Figure 2 In the diagram, 1 is the air inlet, 2 is the smart sensor, 3 is the air outlet, 4 is the spray head, 5 is the spray pipe, 6 is the spray tank, and 7 is the liquid outlet.

[0039] The exhaust gas generated during the drying process of oily sludge enters the desulfurization spray tower through inlet 1. The alkaline spray liquid sprayed by the spray head adsorbs the sulfur-containing acidic gas. The temperature of the gas after spraying and the sulfur concentration in the gas are collected in real time by intelligent sensors to obtain the gas temperature and sulfur concentration at the outlet of the desulfurization spray tower at each moment after spraying.

[0040] In this embodiment, the data acquisition time interval is 5 minutes. As for other implementation methods, the implementer can set it according to the actual situation.

[0041] Furthermore, by installing a pH meter in the spray tank, the actual pH value of the spray liquid in the spray tank at each moment is collected in real time.

[0042] Thus, the gas temperature and sulfur concentration at the outlet of the desulfurization spray tower at various times after spraying, as well as the actual pH value of the spray liquid at each time, are obtained.

[0043] Step 2: Analyze the deviations in gas temperature and sulfur concentration at each time point, and calculate the adsorption coefficient at each time point.

[0044] During the drying process of oily sludge, as the drying temperature increases, a large amount of moisture is evaporated, and the sulfides in the oily sludge also volatilize more rapidly. Furthermore, as the drying temperature in the drying kiln continues to rise, the sulfur content in the exhaust gas will increase significantly. In order to ensure efficient adsorption of sulfur-containing acidic gases in the exhaust gas, the pH value of the alkaline spray solution needs to be dynamically adjusted.

[0045] Secondly, when the alkaline scrubbing liquid comes into contact with the exhaust gas, the greater the temperature difference between the alkaline scrubbing liquid and the residual temperature in the exhaust gas, the more active the gas molecules are, resulting in a larger contact area with the scrubbing liquid and a higher capture capacity for sulfur-containing acidic gases in the exhaust gas. However, when the sulfur concentration in the gas after scrubbing exceeds a preset critical value, the capture capacity for sulfur-containing acidic gases decreases due to limitations in the flow rate of the scrubbing liquid and the contact area between the scrubbing liquid and the exhaust gas. Therefore, by analyzing the changes in temperature and sulfur concentration of the gas after scrubbing at various times, the adsorption coefficient is calculated, specifically as follows:

[0046] The ratio of the gas temperature at each moment to the preset spray liquid temperature is recorded as a relative ratio.

[0047] It should be noted that the temperature of the alkaline spray liquid sprayed by the spray head will affect the dissolution efficiency of sulfuric acid-containing gases. If the temperature is too high, the solubility of sulfuric acid-containing gases will decrease significantly. If the temperature is too low, salt crystallization will occur, clogging the nozzle. Therefore, in this embodiment, the preset spray liquid temperature is 30°C. As for other implementation methods, the implementer can set it according to the actual situation.

[0048] The difference between the preset critical value and the sulfur concentration at each time point is calculated and denoted as the relative difference.

[0049] In this embodiment, since the limit for the fugitive emission concentration of hydrogen sulfide is 0.06 mg / m³, in order to ensure that there is a buffer time for the pH adjustment process of the alkaline spray liquid, the preset critical value is 0.04 mg / m³. As for other implementation methods, the implementer can set it according to the actual situation.

[0050] The product of the relative ratio and the relative difference is used as the adsorption coefficient at each time point;

[0051] It should be noted that a larger relative ratio indicates a greater temperature difference between the alkaline scrubbing liquid and the gas, reflecting a higher adsorption and capture capacity of the alkaline scrubbing liquid for acidic gases such as sulfides in the exhaust gas. A positive and larger relative difference indicates that the detected sulfur concentration is below the critical value, reflecting a high absorption capacity of the alkaline scrubbing liquid. A negative and larger absolute value indicates that the detected sulfur concentration exceeds the critical value, reflecting a decrease in the absorption efficiency of the alkaline scrubbing liquid, which may be accompanied by the risk of exceeding emission standards. Therefore, a positive and larger adsorption coefficient indicates that the sulfur concentration in the gas after spraying is far below the critical value, and the absorption capacity of the alkaline scrubbing liquid in the desulfurization scrubbing tower is sufficient. A negative adsorption coefficient indicates that the sulfur concentration in the gas after spraying exceeds the critical value, and the pH value of the alkaline scrubbing liquid needs to be increased to enhance the absorption efficiency.

[0052] Thus, the adsorption coefficients at each time point are obtained.

[0053] Step 3: By analyzing the gas temperature and sulfur concentration at different times before each time step, as well as the magnitude of the adsorption coefficient, the development coefficient of the grey prediction model is corrected to obtain the corrected development coefficient for each time step. The grey prediction model is then used to predict the sulfur concentration to obtain the predicted concentration for the next time step.

[0054] Furthermore, during the drying process of oily sludge, an increase in drying temperature increases the release of sulfur concentration. To ensure that sulfides in the exhaust gas are absorbed and converted as much as possible, it is necessary to regulate the alkaline scrubbing liquid to increase the contact area between the scrubbing liquid and the corrosive acidic pollutant exhaust gas. The concentration changes of corrosive acidic pollutant exhaust gas under different temperature conditions are predicted. Secondly, during the drying process of oily sludge, the sulfur concentration in the drying exhaust gas changes systematically under increasing temperature conditions. Therefore, by analyzing the correlation between sulfur concentration in the exhaust gas and temperature, the changes in sulfur concentration can be predicted, allowing for subsequent adjustment of the pH value of the alkaline scrubbing liquid based on the prediction results.

[0055] Grey prediction models build mathematical models and make predictions using limited and incomplete information. However, the magnitude of the development coefficient in a grey prediction model affects its predictive performance. The development coefficient describes the development trend of the original data and determines the shape of the time response function in the grey prediction model, thus influencing the trend of the predicted value. When the development coefficient is too large, the exponential term of the time response function decays faster, and the model is more sensitive to data changes. This may lead to a decrease in prediction accuracy due to excessive sensitivity to data fluctuations. Conversely, when the development coefficient is too small, the time response function decays slowly, and the predicted value changes gradually. This may prevent the model from capturing short-term fluctuations in a timely manner, thus affecting prediction accuracy.

[0056] Secondly, in the grey prediction model, the development coefficient is generally taken as 0.5. However, a fixed development coefficient cannot dynamically describe the fluctuation characteristics of the drying tail gas of oily sludge during temperature changes. Therefore, the development coefficient is corrected by observing the changes in gas temperature, thereby more accurately reflecting the changing pattern of sulfur concentration in the gas as the temperature changes. Specifically:

[0057] Each moment and the multiple moments preceding it are recorded as a local time period of each moment;

[0058] In this embodiment, the duration of the local time period is 1 hour. As for other implementation methods, the implementer can set it according to the actual situation.

[0059] Calculate the correlation between gas temperature and sulfur concentration at all times within the local time period;

[0060] In this embodiment, the degree of correlation is measured by calculating the Pearson correlation coefficient between body temperature and sulfur concentration at all times within the local time period. The Pearson correlation coefficient is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as Spearman correlation coefficient, cosine similarity, etc. This embodiment does not impose any special restrictions on this.

[0061] The formula for calculating the corrected development coefficient at each time point is as follows:

[0062]

[0063] in, for The corresponding time corresponds to the corrected development coefficient. for The degree of relevance of time, for The adsorption coefficient at time t. The initial development coefficient is preset;

[0064] In this embodiment, an initial development coefficient is preset. The value is 0.5. As for other implementation methods, the implementer can set it according to the actual situation.

[0065] It should be noted that when the adsorption coefficient is negative, it indicates that the sulfur concentration in the gas after spraying exceeds the critical value. The greater the correlation, the stronger the positive correlation between gas temperature and sulfur concentration. That is, an increase in temperature will lead to an increase in the release of sulfur. However, at this time, the absorption capacity of the spray tower is already saturated and cannot handle sulfur-containing gas exceeding the critical value. Therefore, the development coefficient needs to be adjusted to suppress the predicted concentration of the model, making the prediction results more conservative and stable, and avoiding overestimation of the actual increase in sulfur concentration.

[0066] Based on the sulfur concentration at all times within a local time period at each time point, and the corrected development coefficient, the predicted concentration at the next time point is obtained using a grey prediction model.

[0067] It should be noted that the grey prediction model adopts The model makes predictions, where... The model is a well-known technology and will not be described in detail here.

[0068] At this point, the predicted concentration for the next moment is obtained.

[0069] Step 4: Based on the rate of change of sulfur concentration at each time point and the predicted concentration at the next time point, determine the target pH value corresponding to the next time point; based on the deviation between the actual pH value at each time point and the target pH value corresponding to the next time point, calculate the deviation amount, use a neural network model to obtain the control parameters of the PID control algorithm, and combine the deviation amount at each time point and the change of the deviation amount, use the PID control algorithm to adjust the pH value of the spray liquid in the desulfurization spray tower in real time.

[0070] Furthermore, based on the predicted concentration, the pH value of the alkaline spray solution is dynamically controlled using a PID (Proportional-Integral-Derivative) control algorithm, specifically as follows:

[0071]

[0072] in, for The target at any time value, The preset initial pH value, for Predicted concentration at time, for The sulfur concentration at any given time, This is the normalization function;

[0073] In this embodiment, the preset initial pH value is 10, so that The range of values ​​is To avoid excessive alkali addition during the spraying process, which could cause corrosion to the spray tower, other implementation methods can be set by the implementer according to the actual situation. Secondly, the tanh function is used for normalization. The tanh function is a well-known technology and will not be described in detail here.

[0074] It should be noted that the increase in sulfur concentration reflects the degree of insufficient absorption of sulfides by the scrubbing liquid. By adjusting the increase in sulfur concentration in the exhaust gas, the target pH value can be adjusted to enhance the absorption capacity of the alkaline scrubbing liquid for sulfides and ensure absorption efficiency.

[0075] Compare the actual pH value at each time point with the target pH value at the next time point. The difference between the values ​​is denoted as the deviation.

[0076] Based on the actual pH value at each time, the target pH value for the next time corresponding to each time, and the deviation, the control parameters of the PID control algorithm are tuned using a BP neural network model to obtain the control parameters of the PID algorithm, which include proportional coefficient, integral coefficient, and derivative coefficient.

[0077] In this embodiment, the tuning process of the PID parameters based on the BP neural network model is a well-known technique and will not be described in detail here. Secondly, by collecting the deviations recorded at different times when the pH value of the spray liquid was adjusted in historical periods and the control parameters of the PID algorithm, a training set is formed to train the BP neural network model.

[0078] The flowchart of the method for obtaining control parameters for the PID control algorithm provided in this embodiment is as follows: Figure 3 As shown.

[0079] The difference between the deviation at each time point and the previous time point is denoted as the rate of change of deviation.

[0080] Based on the control parameters, the deviation and the rate of change of deviation are used as inputs to the PID control algorithm to regulate the pH value of the spray liquid at the next moment.

[0081] It should be noted that the PID control algorithm is a well-known technology and will not be elaborated upon here.

[0082] The cooled liquid flows into the oil tank through the desulfurization spray tower and is left to stand in the tank for a period of time to allow it to settle and separate naturally. After natural settling and separation, the upper layer of oil is collected periodically for secondary recycling.

[0083] Based on the same inventive concept as the above methods, this application also provides an environmentally friendly treatment device for drying exhaust gas from oily sludge drying, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for environmentally friendly treatment of drying exhaust gas from oily sludge drying.

[0084] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. An environmentally friendly treatment method for drying off-gas from oil-containing sludge drying, characterized in that, The method includes the following steps: During the drying process of oily sludge, the drying tail gas is introduced into the desulfurization spray tower, and the gas temperature and sulfur concentration at the outlet after spraying, as well as the actual pH value of the spray liquid at each time, are collected in real time. Analyze the deviations in gas temperature and sulfur concentration at different times, and calculate the adsorption coefficient at each time. By analyzing the correlation between gas temperature and sulfur concentration at different times before each time step, as well as the magnitude of the adsorption coefficient, the development coefficient of the grey prediction model is corrected to obtain the corrected development coefficient for each time step. The grey prediction model is then used to predict the sulfur concentration to obtain the predicted concentration for the next time step. Based on the rate of change of sulfur concentration at each time point and the predicted concentration at the next time point, the target pH value at the next time point is determined. Based on the deviation between the actual pH value at each time and the target pH value at the next time, the deviation is calculated, and the control parameters of the PID control algorithm are obtained using a neural network model. Combined with the deviation at each time and the change of the deviation, the pH value of the spray liquid in the desulfurization spray tower is adjusted in real time using the PID control algorithm. The calculation of the adsorption coefficient at each time point includes: The ratio of the gas temperature at each moment to the preset spray liquid temperature is recorded as a relative ratio. The difference between the preset critical value and the sulfur concentration at each time point is calculated and denoted as the relative difference. The relative ratio and the relative difference are combined to determine the adsorption coefficient at each time point; The specific process of the fusion is as follows: the product of the relative ratio and the relative difference is used as the adsorption coefficient at each time point; The obtained modified development coefficients for each time point include: Each moment and the multiple moments preceding it are recorded as a local time period of each moment; Calculate the correlation between gas temperature and sulfur concentration at all times within the local time period; The corresponding corrected development coefficient at each moment The calculation formula is: ,in, for The degree of relevance of time, for The adsorption coefficient at time t. The initial development coefficient is preset; Determining the target pH value at the next moment includes: ,in, for The target pH value at that time. The preset initial pH value, for Predicted concentration at time, for The sulfur concentration at any given time, This is the normalization function.

2. The method for environmentally friendly treatment of drying exhaust gas from oily sludge drying as described in claim 1, characterized in that, The process of obtaining the predicted concentration for the next moment includes: using a grey prediction model to obtain the predicted concentration for the next moment based on the sulfur concentration at all times within the local time period of each moment and the corrected development coefficient.

3. The method for environmentally friendly treatment of drying exhaust gas from oily sludge drying as described in claim 1, characterized in that, The deviation is the difference between the actual pH value at each time point and the target pH value at the next time point.

4. The method for environmentally friendly treatment of drying exhaust gas from oily sludge drying as described in claim 1, characterized in that, The process of obtaining the control parameters of the PID control algorithm includes: using a BP neural network model to tune the control parameters of the PID control algorithm based on the actual pH value at each time, the target pH value at the next time corresponding to each time, and the deviation, thereby obtaining the control parameters of the PID algorithm.

5. The method for environmentally friendly treatment of drying exhaust gas from oily sludge drying as described in claim 1, characterized in that, The real-time control of the pH value of the spray liquid in the desulfurization spray tower includes: recording the difference between the deviation amount at each time and the previous time as the deviation change rate; and using the deviation amount and the deviation change rate as the input of the PID control algorithm based on the control parameters of the PID control algorithm to control the pH value of the spray liquid at the next time.

6. An environmental protection treatment device for the exhaust gas from the drying of oily sludge, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the environmentally friendly treatment method for drying exhaust gas of oily sludge as described in any one of claims 1-5.

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