Automatic sewage treatment method and system

Abstract

The present application relates to wastewater treatment technical field, specifically to a kind of sewage automation processing method and system, the method includes: obtaining the vibration frequency of each collection cycle, centrifugal speed and discharging speed;Progression state coefficient between collection cycle and corresponding progressive collection cycle is constructed, and the concentration progression change coefficient of water to be treated of collection cycle is calculated;Centrifugal deviation coefficient between collection cycle and progressive collection cycle is constructed;And the centrifugal vibration state gain coefficient of each collection cycle is calculated;Based on the above each index, the separation state deviation coefficient of water to be treated of collection cycle is calculated;The window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window size of the window

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater treatment, in particular to an automatic sewage treatment method and system. BACKGROUND

[0002] Sintering machine head ash is captured by the sintering flue gas of a steel enterprise through a large flue to an electric precipitator, and the sintering machine head ash contains elements such as sodium and potassium. Due to the enrichment of alkali metals in the sintering machine head ash, the sintering machine head ash entering the blast furnace system will erode the blast furnace bottom and lining, and seriously affect the service life of the blast furnace. Therefore, the treatment of the sintering machine head ash is very important. At present, the main treatment methods of the sintering machine head ash are water washing and water washing liquid evaporation, but the water washing water is ordinary production water, and there is a large amount of concentrated brine in the plant. The concentrated brine is used for ironmaking slag flushing and steelmaking slag flushing, but the water slag can be used as a building material, and there is a strict requirement for chloride ions in the water slag. Using concentrated brine to flush slag will not meet the requirements of building material use, resulting in no place to go for the concentrated brine.

[0003] The treatment effect of the sintering machine head ash by using ordinary production water is poor, and a large amount of concentrated brine is accumulated in the plant. Separate treatment of the sintering machine head ash and the concentrated brine will increase the cost of the enterprise. At present, the treatment process of the sintering machine head ash realizes automatic treatment, but the deviation of parameter control in the automatic treatment process will cause problems such as low production efficiency, evaporation crystallization salt blocking, and large amount of filtrate treatment. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide an automatic sewage treatment method and system, and the technical scheme adopted is as follows:

[0005] In a first aspect, the present application provides an automatic sewage treatment method, which comprises the following steps:

[0006] The centrifuge vibration frequency, centrifuge speed and discharging speed of each collection period are respectively composed into a vibration frequency sequence, a centrifuge speed sequence and a discharging speed sequence of each collection period;

[0007] The Bayesian variable point detection algorithm is used to obtain a discharging stability curve of the discharging speed sequence; the discharging stability curve obtaining method is used to obtain a centrifugal speed stability curve of the centrifugal speed sequence; N collection periods after each collection period are taken as progressive collection periods of the collection period; a progressive state coefficient is obtained according to a difference between the maximum data in the discharging stability curve of the collection period and the corresponding progressive collection period; a to-be-treated water concentration progressive change coefficient of the discharging speed sequence of the collection period is obtained according to the KL divergence between the discharging speed sequence of the collection period and the discharging speed sequence of each progressive collection period and the progressive state coefficient; a to-be-treated water centrifugal deviation coefficient between the collection period and the progressive collection period is obtained according to the KL divergence between the discharging stability curve of the collection period and the centrifugal speed stability curve of the progressive collection period; a centrifugal deviation state vector and a discharging state progressive vector of each collection period are obtained; vibration frequency sequences of all the progressive collection periods of the collection period are taken as centrifugal vibration comparison sequences, the empirical mode decomposition algorithm is used to obtain residual components of the centrifugal vibration comparison sequences, and a centrifugal vibration state gain coefficient of the collection period is obtained according to the maximum value and the minimum value of the residual components; a to-be-treated water separation state deviation coefficient of the collection period is obtained according to the centrifugal deviation state vector, the discharging state progressive vector and the centrifugal vibration state gain coefficient of the collection period; the to-be-treated water separation state deviation coefficients of all the collection periods are taken as inputs of an LSTM neural network, and the LSTM neural network outputs a window size of a LOWESS algorithm;

[0008] The LOWESS algorithm is used to combine the vibration frequency sequence, the centrifugal speed sequence and the discharging speed sequence to respectively fit a vibration state trend fitting curve, a centrifugal state trend fitting curve and a discharging state trend fitting curve; a centrifugal state deviation parameter is obtained by combining the maximum value and the average value of each state trend fitting curve; when the centrifugal state deviation parameter is greater than an adjustment threshold value, the centrifugal machine state is abnormal, and the centrifugal machine speed and the discharging speed are manually adjusted.

[0009] Preferably, the Bayesian variable point detection algorithm is used to obtain the discharging stability curve of the discharging speed sequence, including:

[0010] The Bayesian variable point detection algorithm is used to obtain a mutation point probability of each element in the discharging speed sequence, the mutation point probability of each element is taken as a vertical coordinate, a collection time of each element is taken as a horizontal coordinate, and a curve formed by fitting the horizontal coordinate and the vertical coordinate is taken as the discharging stability curve of the discharging speed sequence.

[0011] Preferably, the progressive state coefficient is obtained according to a difference between the maximum data in the discharging stability curve of the collection period and the corresponding progressive collection period, and the expression is:

[0012]

[0013] In the formula, C x，x+nω represents an adjustment parameter; max(a x ), max(a x+n ) represent the maximum values of the feed speed sequences a x , a x+n corresponding to the xth and x+nth progressive collection periods, respectively.

[0014] Preferably, the progressive change coefficient of the concentration of the treated water in the feed speed sequence of the collection period comprises:

[0015] The progressive state coefficient between the feed speed sequence of the collection period and the feed speed sequence of each progressive collection period is obtained, the KL divergence is obtained, the product of the progressive state coefficient and the KL divergence is calculated, and the average of all the products of the collection period is taken as the progressive change coefficient of the concentration of the treated water in the feed speed sequence of the collection period.

[0016] Preferably, the centrifugal deviation coefficient of the treated water between the collection period and the progressive collection period is obtained according to the KL divergence between the feed stability curve of the collection period and the centrifugal speed stability curve of the progressive collection period, and comprises:

[0017] The KL divergence between the feed stability curve of the collection period and the centrifugal speed stability curve of the progressive collection period is obtained; and the KL divergence is taken as the centrifugal deviation coefficient of the treated water between the collection period and the progressive collection period.

[0018] Preferably, the centrifugal deviation state vector of the collection period and the feed state progressive vector are obtained, and comprise: the centrifugal deviation state vector of the collection period is composed of the centrifugal deviation of the treated water of the collection period and each progressive collection period; and the feed state progressive vector of the collection period is composed of the progressive change coefficient of the concentration of the treated water of the collection period and each progressive collection period.

[0019] Preferably, the centrifugal vibration state gain coefficient of the collection period is the difference between the maximum value and the minimum value of the residual component.

[0020] Preferably, the deviation coefficient of the separation state of the treated water of the collection period is obtained according to the centrifugal deviation state vector, the feed state progressive vector and the centrifugal vibration state gain coefficient of the collection period, and specifically comprises:

[0021] The cosine similarity between the centrifugal deviation state vector and the feed state progressive vector of the collection period is calculated, and the product of the centrifugal vibration state gain coefficient and the cosine similarity is taken as the deviation coefficient of the separation state of the treated water of the collection period.

[0022] Preferably, the centrifugal state deviation parameter is obtained by combining the maximum value and the average value of each state trend fitting curve.

[0023] Obtaining the maximum value and the average value of each state trend fitting curve, calculating the difference between the maximum value and the average value of each state trend fitting curve, obtaining the ratio of the difference and the average value, and taking the average value of all the ratios of all state trend fitting curves as the centrifugal state deviation parameter.

[0024] In a second aspect, the embodiments of the present application also provide a sewage automatic treatment system, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method in any of the above aspects when executing the computer program.

[0025] The present application has at least the following beneficial effects:

[0026] The present application realizes the comprehensive treatment of concentrated brine and sintering machine head ash by taking the concentrated brine generated by the enterprise as the washing water in the sintering machine head ash treatment process. The concentration progressive change coefficient of the water to be treated, the centrifugal deviation coefficient of the water to be treated, and the centrifugal vibration state gain coefficient are constructed, and then the centrifugal deviation state vector and the unloading state progressive vector are obtained. Based on this, the treated water separation state deviation coefficient is constructed to obtain the sliding window parameter. Based on the sliding window parameter, the LOWESS algorithm is used to obtain the vibration state trend fitting curve, the centrifugal state trend fitting curve, and the unloading state trend fitting curve. The centrifugal state deviation parameter is obtained, and the speed and unloading speed in the centrifugal treatment in the evaporation and salt separation module are controlled. The beneficial effect is to avoid the problems of blockage and unstable state of the centrifuge in the evaporation and salt separation module process, improve the precision of the comprehensive treatment of concentrated brine and sintering machine head ash, and realize the efficiency of concentrated brine sewage treatment. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0028] Figure 1 A step flowchart of a sewage automatic treatment method provided by an embodiment of the present application is shown in the figure.

[0029] Figure 2 A process flow diagram for treating sintering machine head ash with concentrated brine is shown in the figure. DETAILED DESCRIPTION

[0030] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific implementation, structure, features and effects of the sewage automatic treatment method and system according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0031] 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 the present application belongs.

[0032] The specific scheme of the sewage automatic treatment method and system provided by the present application is specifically described below in combination with the drawings.

[0033] Please refer to Figure 1 which shows the step flowchart of a sewage automatic treatment method provided by one embodiment of the present application, which includes the following steps:

[0034] In step S001, the concentrated brine and the sintering machine head ash are comprehensively treated, and relevant monitoring data in the comprehensive treatment process are obtained.

[0035] Since the washing water in the treatment process of the sintering machine head ash has poor treatment effect with ordinary production water, and at the same time there is a large amount of concentrated brine to be treated in the factory, in order to achieve the goal of zero emission and reduce the operating cost, the washing water in the treatment process of the sintering machine head ash can be replaced by the concentrated brine, and the treatment of the concentrated brine is carried out at the same time of treating the sintering machine head ash, that is, the problem of treating the concentrated brine in the factory is solved under the condition of increasing small investment, and the concentrated brine can achieve better treatment effect of the sintering machine head ash than the ordinary production water.

[0036] The comprehensive treatment process flow of the sintering machine head ash and the concentrated brine mainly includes: a machine head ash storage module, a washing module, a water washing pretreatment module, an evaporation and salt separation module, a crystalline salt drying module, and a packaging module. The process flow of treating the sintering machine head ash with the concentrated brine is shown in Figure 2 Firstly, the sintering machine head ash in the production process of the enterprise is collected through the machine head ash storage module, and the collected sintering machine head ash is cleaned with the concentrated brine in the factory as washing water, that is, the mixed wastewater of the comprehensive treatment of the concentrated brine and the sintering machine head ash can be obtained through the washing module, and the untreated water of the sintering machine head ash and the residue filter cake of the concentrated brine can be obtained by passing the mixed wastewater through the water washing pretreatment module, wherein the residue filter cake can be transported to the factory as raw material for recycling. Further, the untreated water needs to be treated by evaporation crystallization and centrifugal separation in the evaporation and salt separation module, and since the treatment of the evaporation and salt separation module is directly related to the final production efficiency, the control of the related parameters in the treatment process can improve the treatment efficiency and quality.

[0037] Specifically, the embodiment adopts a vibration sensor and a speed sensor to collect vibration frequency data, centrifugal speed data and discharging speed data of the centrifuge, the number of vibration frequency data, centrifugal speed data and discharging speed data obtained in each collection cycle is y, the time interval of collecting data is 1 min, and the number of collection cycles is m, and the time interval, the number of collection cycles and the number of collected data can be set by the implementer, which is not limited in the embodiment. The vibration frequency data, centrifugal speed data and discharging speed data corresponding to each collection cycle are respectively arranged in a sequence in ascending order of time to form a vibration frequency sequence, a centrifugal speed sequence and a discharging speed sequence.

[0038] In step S002, the concentration progressive change coefficient of the water to be treated and the centrifugal deviation coefficient of the water to be treated are obtained according to the progressive change relationship of the monitoring data in the evaporation and salt separation module, the separation state deviation coefficient of the water to be treated is constructed based on each index, and then the window size of the LOWESS algorithm fitting process is adaptively obtained, and each state trend fitting curve is obtained.

[0039] After the above processing, the relevant monitoring data of the evaporation and salt separation module in the comprehensive treatment process of the sintering machine head ash and concentrated brine can be obtained, and the monitoring data includes a vibration frequency sequence, a centrifugal speed sequence and a discharging speed sequence. In the comprehensive treatment process of the sintering machine head ash and concentrated brine, there are problems such as evaporation crystalline salt blockage, low production efficiency, large amount of filtrate treatment, etc. The existence of these problems affects the efficiency and effect of the treatment, among which the unstable speed of the centrifuge during operation may lead to poor separation effect, and in severe cases, it may cause separation channel blockage. Further, if the centrifuge speed is unstable, it may cause the centrifuge to vibrate and even be stuck, affecting the production efficiency. Therefore, the adjustment of the relevant parameters of the evaporation and salt separation module during operation is one of the important parts in the comprehensive treatment process of the sintering machine head ash and concentrated brine.

[0040] Further, the water to be treated can be obtained through the head ash storage module, the water washing module and the water washing pretreatment module, and when the concentration of the water to be treated is high, it enters the centrifuge after evaporation and crystallization treatment. The unstable discharging speed of the centrifuge may lead to poor separation effect of the centrifuge, and the large running load of the centrifuge may lead to unstable centrifugal speed, and in severe cases, it may block the transportation channel of the separation material. Therefore, the progressive change state of the mixed solution concentration is reflected by the change of the discharging speed of the water to be treated after evaporation and crystallization.

[0041] Specifically, for the discharging speed sequence A x , the input is the discharging speed sequence A x , and the Bayesian variable point detection algorithm is used to obtain the discharging speed sequence A xa mutation point probability distribution diagram in which a probability of each element in the unloading speed sequence being a mutation point is represented, the probability of each element being a mutation point is taken as a vertical coordinate, a collection time corresponding to each element is taken as a horizontal coordinate, and a curve formed by fitting the vertical coordinate and the horizontal coordinate is taken as the unloading speed sequence A x a unloading stability curve a of x The Bayesian time series mutation monitoring algorithm is a known technology and will not be described in detail. Further, since there is a progressive relationship between the changes in the unloading speed and the rotational speed of the centrifuge, N progressive collection cycles after the xth collection cycle are obtained, and N is 5. The progressive change coefficient of the water concentration to be treated is calculated according to the unloading stability curve, and the specific calculation formula is as follows:

[0042]

[0043] In the formula, β x represents the progressive change coefficient of the water concentration to be treated corresponding to the unloading speed sequence of the xth collection cycle; max(a x ), max(a x+n ) respectively represent the maximum values of the unloading stability curves of the unloading speed sequences a x , a x+ corresponding to the xth and x+nth progressive collection cycles; C x，x+n represents a progressive state coefficient between the unloading speed sequences corresponding to the xth and x+nth collection cycles; ω represents an adjustment parameter, which is used to avoid a zero denominator, and an empirical value of 0.1 is taken; KL(a x , a x+n ) represents the KL divergence between a x and a x+n ; and N represents the number of progressive collection cycles, and is 5.

[0044] If the unloading speed sequence A x corresponding to the xth collection cycle has a greater possibility of state change relative to the unloading speeds of other collection cycles, the value of max(a x )-max(a x+n ) obtained by calculation is greater, that is, the value of C x，x+n is greater, further, the difference between the unloading stability curve of the unloading speed sequence A x corresponding to the xth collection cycle and the unloading stability curves corresponding to other collection cycles is greater, that is, the value of KL(a x , a x+n ) obtained by calculation is greater, and the progressive change coefficient β xThe greater, the greater the possibility of change of the unloading speed state of the centrifuge in the time period corresponding to the xth acquisition cycle, and the greater the change in the concentration of the water to be treated that is processed, which affects the running state of the centrifuge.

[0045] Further, the change of the unloading speed of the centrifuge or the increase of the concentration of the water to be treated can cause the change of the centrifuge speed, resulting in unstable running state of the centrifuge and increased vibration frequency of the centrifuge, so the state progressive change characteristics of the centrifuge can be analyzed through the state change of the unloading speed and the change of the centrifuge speed.

[0046] Specifically, the centrifuge speed sequence B corresponding to the xth acquisition cycle is acquired x , the unloading speed sequence A corresponding to the xth acquisition cycle is acquired x , and the unloading stability curve a of the unloading speed sequence A is acquired x in the same way as the acquisition mode of the centrifuge speed stability curve b of the centrifuge speed sequence B corresponding to the xth acquisition cycle x .

[0047] γ x，x+n = KL(a x , b x+n )

[0048] In the formula, γ x，x+n represents the centrifugal deviation coefficient of the water to be treated between the xth and the x+nth acquisition cycles; a x represents the unloading stability curve of the unloading speed sequence corresponding to the xth acquisition cycle; b x+n represents the centrifuge speed stability curve of the centrifuge speed sequence corresponding to the x+nth acquisition cycle; and KL(a x , b x+n ) represents the KL divergence of a x and b x+n .

[0049] If the state deviation of the unloading speed sequence corresponding to the xth acquisition cycle and the centrifuge speed sequence corresponding to the x+nth acquisition cycle is large, the value of the centrifugal deviation coefficient γ x，x+n of the water to be treated between the xth and the x+nth acquisition cycles calculated is greater. The x+1th to the x+nth acquisition cycles are progressive acquisition cycles of the xth acquisition cycle.

[0050] Further, the centrifugal deviation coefficients of the water to be treated between the xth acquisition cycle and each progressive acquisition cycle are acquired respectively, and then a centrifugal deviation state vector P is formed in ascending order of time xThe concentration progressive change coefficient of the water to be treated corresponding to the xth acquisition cycle and each progressive acquisition cycle is arranged in ascending order of time to form a progressive vector of discharging state Q x The progressive relationship between the discharging speed and the rotating speed of the centrifuge is reflected by the centrifugal deviation state vector and the progressive vector of discharging state.

[0051] Further, all the vibration frequency data corresponding to the n progressive acquisition cycles are taken as a centrifugal vibration contrast sequence, which is input as a centrifugal vibration contrast sequence, and an empirical mode decomposition algorithm is used to obtain a residual component of the centrifugal vibration contrast sequence, and the difference between the maximum value and the minimum value of the residual component is taken as a centrifugal vibration state gain coefficient f x of the xth acquisition cycle. The vibration frequency growth trend in the time period corresponding to the n progressive acquisition cycles after the xth acquisition cycle is reflected by the centrifugal vibration state gain coefficient. The specific implementation process of the empirical mode decomposition algorithm is a known technology and will not be described in detail.

[0052] The separation state deviation coefficient of the water to be treated is calculated according to the centrifugal deviation state vector, the progressive vector of discharging state and the centrifugal vibration state gain coefficient. The progressive deviation relationship between the discharging speed, the rotating speed and the vibration frequency of the centrifuge corresponding to each acquisition cycle is reflected by the separation state deviation coefficient of the water to be treated. The specific calculation formula is as follows:

[0053] θ x =f x *Y(P x ,Q x )

[0054] In the formula, θ x represents the separation state deviation coefficient of the water to be treated in the xth acquisition cycle; f x represents the centrifugal vibration state gain coefficient in the xth acquisition cycle; P x and Q x respectively represent the centrifugal deviation state vector and the progressive vector of discharging state in the xth acquisition cycle; Y(P x , Q x ) represents the calculation of the cosine similarity between P x and Q x .

[0055] If the centrifuge state starts to deviate in the xth acquisition cycle, the values of f x and Y(P x , Q x ) are larger, that is, the value of the separation state deviation coefficient θ x of the water to be treated in the xth acquisition cycle is larger, which indicates that the risk of the centrifuge state change is larger.

[0056] Further, a sequence composed of the state deviation parameters of the to-be-processed water corresponding to all the collection periods is taken as a sliding window parameter comparison sequence. Then, the window size in the LOWESS algorithm is predicted in combination with a prediction model. The sliding window parameter comparison sequence is taken as the input of an LSTM neural network, and the output of the LSTM neural network is the window size span. The LSTM neural network model and the training process are prior art, and will not be described herein.

[0057] At this point, the window size in the LOWESS algorithm fitting curve process can be adaptively predicted according to the above process of the embodiment, the precision of curve fitting can be improved, and the accuracy of data analysis can be ensured. Further, all the vibration frequency data, centrifugal speed data and discharging speed data corresponding to m collection periods are arranged in ascending order of time to form a vibration frequency sequence, a centrifugal speed sequence and a discharging speed sequence, respectively, which are input into the LOWESS algorithm for curve fitting. The window size in the LOWESS algorithm fitting process is span, and the vibration state trend fitting curve L1, the centrifugal state trend fitting curve L2 and the discharging state trend fitting curve L3 are obtained by using the LOWESS algorithm. The specific implementation process of the LOWESS algorithm is a known technology, and will not be described herein.

[0058] In step S003, the state trend fitting curves are analyzed to comprehensively process the state abnormal change, and the analysis result is used to adjust and realize stable processing in a timely manner.

[0059] The centrifugal state deviation parameter is calculated according to the vibration state trend fitting curve L1, the centrifugal state trend fitting curve L2 and the discharging state trend fitting curve L3. The specific calculation formula is as follows:

[0060]

[0061] In the formula, L represents the centrifugal state deviation parameter; L w represents the wth state trend fitting curve; max(L w ) and μ(L w ) represent the maximum value and the average value of L w , respectively; t represents the number of state trend fitting curves, and t=3.

[0062] If the state of the evaporation and salt separation module centrifuge is abnormal, the value of the calculated centrifugal state deviation parameter L is larger, the speed of the centrifuge and the discharging speed need to be adjusted in time to avoid damage to the equipment; the adjustment threshold is set to W = 0.1, which can be set by the implementer, and when the centrifugal state deviation parameter is greater than the adjustment threshold, the equipment condition needs to be checked, and the relevant operating personnel adjust the speed of the centrifuge and the discharging speed in time. The embodiment aims to detect the comprehensive treatment process of the concentrated brine and sintering machine head ash, to accurately detect the state of the centrifuge in the treatment process, and to avoid problems such as poor treatment effect caused by abnormal centrifuge in the treatment process.

[0063] Further, the evaporation and salt separation module in the comprehensive treatment process of the sintering machine head ash and the concentrated brine can be monitored through the above treatment, the separated sodium chloride and potassium chloride from the treated water are obtained, and the separated sodium chloride and potassium chloride are treated through the crystalline salt drying module and the packaging module to obtain the corresponding products, realizing the reuse of the concentrated brine, and the reuse of the mixed wastewater in the comprehensive treatment process of the concentrated brine and the sintering machine head ash.

[0064] Specifically, the accumulated concentrated brine in the factory is used as the water washing water of the sintering machine head ash, the concentrated brine obtained through the water washing module and the sintering machine head ash comprehensive treatment mixed wastewater, the mixed wastewater is treated through the water washing pretreatment module to obtain treated water and residue filter cake, the residue filter cake can be recycled as raw material, and the treated water is treated through the evaporation and salt separation module, the crystalline salt drying module and the packaging module to obtain the byproduct of the treated water.

[0065] Based on the same inventive concept as the above method, the embodiment of the present application also provides a sewage automatic treatment system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above sewage automatic treatment methods when executing the computer program.

[0066] In summary, the embodiment of the present application realizes the comprehensive treatment of the concentrated brine and the sintering machine head ash by using the concentrated brine generated by the enterprise as the water washing water in the sintering machine head ash treatment process. The concentration progressive change coefficient of the treated water, the centrifugal deviation coefficient of the treated water and the centrifugal vibration state gain coefficient are constructed, and then the centrifugal deviation state vector and the discharging state progressive vector are obtained. Based on this, the treated water separation state deviation coefficient is constructed to obtain the sliding window parameter, and the vibration state trend fitting curve, the centrifugal state trend fitting curve and the discharging state trend fitting curve are obtained by using the LOWESS algorithm based on the sliding window parameter. The centrifugal state deviation parameter is obtained, and the speed and discharging speed in the centrifugal treatment of the evaporation and salt separation module are controlled. The beneficial effect is to avoid the problems of blockage and unstable state of the centrifuge in the evaporation and salt separation module process, improve the precision of the comprehensive treatment of the concentrated brine and the sintering machine head ash, and realize the reuse of the concentrated brine.

[0067] It should be noted that the above-mentioned embodiment sequence of the present application is only for description, not representing the advantages and disadvantages of the embodiments. And the above-mentioned description is for specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0068] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0069] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for automatic treatment of sewage, characterized in that, The method comprises the following steps: The centrifuge vibration frequency, the centrifugal speed, and the discharging speed of each collection period are respectively composed of the vibration frequency sequence, the centrifugal speed sequence, and the discharging speed sequence of each collection period; The discharging stable curve of the discharging speed sequence is obtained by using the Bayesian variable point detection algorithm; the centrifugal speed stable curve is obtained by using the method for obtaining the discharging stable curve on the centrifugal speed sequence; the next N collection periods of each collection period are regarded as the progressive collection periods of each collection period; the progressive state coefficient is obtained according to the difference between the maximum data in the discharging stable curves of the collection period and the corresponding progressive collection period, so as to obtain the progressive change coefficient of the concentration of the treated water in the discharging speed sequence of the collection period; the centrifugal deviation coefficient between the collection period and the progressive collection period is obtained according to the KL divergence between the discharging stable curve of the collection period and the centrifugal speed stable curve of the progressive collection period; the centrifugal deviation state vector and the discharging state progressive vector of each collection period are obtained through the centrifugal deviation coefficient and the concentration progressive change coefficient; the vibration frequency sequence of all the progressive collection periods of the collection period is regarded as the centrifugal vibration comparison sequence, the residual component of the centrifugal vibration comparison sequence is obtained by using the empirical mode decomposition algorithm, and the centrifugal vibration state gain coefficient of the collection period is obtained according to the maximum value and the minimum value of the residual component; the separation state deviation coefficient of the treated water of the collection period is obtained according to the centrifugal deviation state vector, the discharging state progressive vector, and the centrifugal vibration state gain coefficient of the collection period; Based on the separation state deviation coefficient of the treated water, the window size of the LOWESS algorithm fitting process is adaptively obtained; the vibration state trend fitting curve, the centrifugal state trend fitting curve, and the discharging state trend fitting curve are respectively fitted by using the LOWESS algorithm combined with the vibration frequency sequence, the centrifugal speed sequence, and the discharging speed sequence; the centrifugal state deviation parameter is obtained by combining the maximum value and the average value of each state trend fitting curve, and when the centrifugal state deviation parameter is greater than the adjustment threshold value, the centrifuge state is abnormal, and the centrifuge speed and the discharging speed are manually adjusted.

2. The method of claim 1, wherein the method comprises: The discharging stable curve of the discharging speed sequence is obtained by using the Bayesian variable point detection algorithm, which comprises the following steps: The mutation point probability of each element in the discharging speed sequence is obtained by using the Bayesian variable point detection algorithm, the mutation point probability of each element is taken as the ordinate, the collection time of each element is taken as the abscissa, and the curve formed by fitting the abscissa and the ordinate is taken as the discharging stable curve of the discharging speed sequence.

3. The method of claim 1, wherein the method comprises: The expression for obtaining the progressive state coefficient according to the difference between the maximum data in the discharging stable curves of the collection period and the corresponding progressive collection period is as follows: In the formula, denotes the progression state coefficient between the discharging speed sequence corresponding to the first acquisition cycle and the first progressive acquisition cycle; denotes the adjustment parameter; , denote the maximum value of the discharging stability curve of the discharging speed sequence corresponding to the first, the second progressive acquisition cycle .

4. The method of claim 1, wherein the method comprises: The progressive change coefficient of the concentration of the treated water in the discharging speed sequence of the collection period comprises the following steps: The progressive state coefficient and the KL divergence between the discharging speed sequence of the collection period and each progressive collection period are obtained, the product of the progressive state coefficient and the KL divergence is calculated, and the average value of all the products of the collection period is taken as the progressive change coefficient of the concentration of the treated water in the discharging speed sequence of the collection period.

5. The automated wastewater treatment method as described in claim 1, characterized in that, The KL divergence between the unloading stability curve of the collection cycle and the progressive collection cycle centrifugal speed stability curve is obtained, and a centrifugal deviation coefficient of the water to be treated between the collection cycle and the progressive collection cycle is obtained. The KL divergence between the unloading stability curve of the collection cycle and the progressive collection cycle centrifugal speed stability curve is obtained, and a centrifugal deviation coefficient of the water to be treated between the collection cycle and the progressive collection cycle is obtained.

6. The method of claim 1, wherein the method further comprises: determining a water quality of the wastewater; and determining a water quality of the treated wastewater. The centrifugal deviation state vector of each collection cycle and the unloading state progressive vector are obtained, including: the centrifugal deviation coefficients of the water to be treated of the collection cycle and each progressive collection cycle are combined to form the centrifugal deviation state vector of the collection cycle; and the concentration progressive change coefficients of the water to be treated of the collection cycle and each progressive collection cycle are combined to form the unloading state progressive vector of the collection cycle.

7. The method of claim 1, wherein the method further comprises: determining a water quality of the wastewater; and determining a water quality of the treated wastewater. The centrifugal vibration state gain coefficient of the collection cycle is the difference between the maximum value and the minimum value of the residual component.

8. The method of claim 1, wherein the method further comprises: determining a water quality of the wastewater; and determining a water quality of the treated wastewater. The centrifugal deviation state vector of the collection cycle, the unloading state progressive vector, and the centrifugal vibration state gain coefficient are used to obtain a separation state deviation coefficient of the water to be treated of the collection cycle, including: The cosine similarity between the centrifugal deviation state vector of the collection cycle and the unloading state progressive vector is calculated, and the product of the centrifugal vibration state gain coefficient and the cosine similarity is taken as the separation state deviation coefficient of the water to be treated of the collection cycle.

9. The method of claim 1, wherein the method further comprises: determining a water quality of the wastewater; and determining a water quality of the treated wastewater. The maximum value and the mean value of each state trend fitting curve are obtained, the difference between the maximum value and the mean value of each state trend fitting curve is calculated, the ratio of the difference to the mean value is obtained, and the mean value of all the ratios of all the state trend fitting curves is taken as the centrifugal state deviation parameter. The processor executes the computer program to realize the steps of the method of any one of claims 1-9.

10. An automated wastewater treatment system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, ​

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