CN117547843A - Comprehensive utilization method and system for strong brine and sintering machine head ash - Google Patents
Comprehensive utilization method and system for strong brine and sintering machine head ash Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 93
- 238000005245 sintering Methods 0.000 title claims abstract description 39
- 239000012267 brine Substances 0.000 title claims abstract description 35
- HPALAKNZSZLMCH-UHFFFAOYSA-M sodium;chloride;hydrate Chemical compound O.[Na+].[Cl-] HPALAKNZSZLMCH-UHFFFAOYSA-M 0.000 title claims abstract description 35
- 238000001704 evaporation Methods 0.000 claims abstract description 341
- 230000008020 evaporation Effects 0.000 claims abstract description 338
- 238000012544 monitoring process Methods 0.000 claims abstract description 163
- 230000002159 abnormal effect Effects 0.000 claims abstract description 85
- 239000007864 aqueous solution Substances 0.000 claims abstract description 39
- 230000008569 process Effects 0.000 claims abstract description 36
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 31
- 238000005406 washing Methods 0.000 claims abstract description 18
- 238000004220 aggregation Methods 0.000 claims abstract description 17
- 230000002776 aggregation Effects 0.000 claims abstract description 17
- 150000003839 salts Chemical class 0.000 claims abstract description 15
- 238000001035 drying Methods 0.000 claims abstract description 12
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- 238000000354 decomposition reaction Methods 0.000 claims abstract description 9
- 238000000605 extraction Methods 0.000 claims abstract description 7
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- 238000005086 pumping Methods 0.000 claims abstract description 4
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- 238000004422 calculation algorithm Methods 0.000 claims description 18
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- 238000005259 measurement Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 3
- 208000015181 infectious disease Diseases 0.000 abstract 1
- 239000007789 gas Substances 0.000 description 77
- WCUXLLCKKVVCTQ-UHFFFAOYSA-M Potassium chloride Chemical compound [Cl-].[K+] WCUXLLCKKVVCTQ-UHFFFAOYSA-M 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 9
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 8
- 208000028659 discharge Diseases 0.000 description 8
- 238000001556 precipitation Methods 0.000 description 7
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 239000000047 product Substances 0.000 description 6
- 238000012546 transfer Methods 0.000 description 6
- 238000010438 heat treatment Methods 0.000 description 5
- 239000001103 potassium chloride Substances 0.000 description 5
- 235000011164 potassium chloride Nutrition 0.000 description 5
- 239000007787 solid Substances 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- 238000003825 pressing Methods 0.000 description 4
- 239000011780 sodium chloride Substances 0.000 description 4
- 238000005260 corrosion Methods 0.000 description 3
- 230000007797 corrosion Effects 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 3
- 239000007788 liquid Substances 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 229910052700 potassium Inorganic materials 0.000 description 3
- XAEFZNCEHLXOMS-UHFFFAOYSA-M potassium benzoate Chemical compound [K+].[O-]C(=O)C1=CC=CC=C1 XAEFZNCEHLXOMS-UHFFFAOYSA-M 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000002893 slag Substances 0.000 description 3
- 239000010802 sludge Substances 0.000 description 3
- 239000002002 slurry Substances 0.000 description 3
- 159000000000 sodium salts Chemical class 0.000 description 3
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 229910052801 chlorine Inorganic materials 0.000 description 2
- 239000000460 chlorine Substances 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 239000000571 coke Substances 0.000 description 2
- 238000009833 condensation Methods 0.000 description 2
- 230000005494 condensation Effects 0.000 description 2
- 238000002425 crystallisation Methods 0.000 description 2
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- 230000000171 quenching effect Effects 0.000 description 2
- 238000004064 recycling Methods 0.000 description 2
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- 238000004062 sedimentation Methods 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 description 1
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 229910052783 alkali metal Inorganic materials 0.000 description 1
- 150000001340 alkali metals Chemical class 0.000 description 1
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- 230000000903 blocking effect Effects 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- BRPQOXSCLDDYGP-UHFFFAOYSA-N calcium oxide Chemical compound [O-2].[Ca+2] BRPQOXSCLDDYGP-UHFFFAOYSA-N 0.000 description 1
- 239000000292 calcium oxide Substances 0.000 description 1
- ODINCKMPIJJUCX-UHFFFAOYSA-N calcium oxide Inorganic materials [Ca]=O ODINCKMPIJJUCX-UHFFFAOYSA-N 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
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- 238000006297 dehydration reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 239000000706 filtrate Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000004868 gas analysis Methods 0.000 description 1
- 150000004820 halides Chemical class 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 229910021645 metal ion Inorganic materials 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
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- 238000010606 normalization Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- CHWRSCGUEQEHOH-UHFFFAOYSA-N potassium oxide Chemical compound [O-2].[K+].[K+] CHWRSCGUEQEHOH-UHFFFAOYSA-N 0.000 description 1
- 229910001950 potassium oxide Inorganic materials 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 239000013049 sediment Substances 0.000 description 1
- 229910052708 sodium Inorganic materials 0.000 description 1
- 239000011734 sodium Substances 0.000 description 1
- KKCBUQHMOMHUOY-UHFFFAOYSA-N sodium oxide Chemical compound [O-2].[Na+].[Na+] KKCBUQHMOMHUOY-UHFFFAOYSA-N 0.000 description 1
- 229910001948 sodium oxide Inorganic materials 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000011343 solid material Substances 0.000 description 1
- 238000003756 stirring Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 238000009834 vaporization Methods 0.000 description 1
- 230000008016 vaporization Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
- 239000011701 zinc Substances 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D1/00—Evaporating
- B01D1/0082—Regulation; Control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D1/00—Evaporating
- B01D1/26—Multiple-effect evaporating
Landscapes
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Manufacture And Refinement Of Metals (AREA)
- Vaporization, Distillation, Condensation, Sublimation, And Cold Traps (AREA)
Abstract
The application relates to the technical field of water treatment, and provides a comprehensive utilization method and system of strong brine and sintering machine head ash, wherein the comprehensive utilization method comprises the following steps: sending the sintering machine head ash captured by the electric dust collector into an ash collecting bin by using a negative pressure pumping tank truck, and sequentially carrying out water washing, mud-water separation and impurity removal pretreatment on the sintering machine head ash by using a water washing tank to obtain an aqueous solution, and conveying the aqueous solution to a multi-effect evaporator by using a feeding pump; determining an evaporation balance coefficient based on a decomposition result of the temperature time sequence curve and the pressure time sequence curve in the evaporation window; determining the concentration of non-condensable gas aggregation based on abnormal conditions of temperature and pressure data analyzed by the evaporation balance coefficient and abnormal moments; determining a noncondensable gas emission decision based on the concentration of noncondensable gas aggregate in each monitoring window in all evaporation areas in the multi-effect evaporator; the evaporated aqueous solution is then subjected to salt extraction and drying. According to the method, the discharge of noncondensable gas in the evaporation area is controlled in real time, so that the infection efficiency in the evaporation process of the aqueous solution is improved.
Description
Technical Field
The application relates to the technical field of water treatment, in particular to a comprehensive utilization method and system of strong brine and sintering machine head ash.
Background
The sintering machine head ash refers to dust captured by sintering flue gas in an electric dust collector through a large flue in a sintering process, and the dust has extremely fine granularity and contains harmful elements such as K, na, cl and the like. For the head ash of the sintering machine, two methods of recycling and piling and discarding are selected in the current steel mill, and dust pollution is caused by piling and discarding; because the nose ash contains a large amount of halides and heavy metals, soil and water source pollution can be caused. The recycling can cause alkali metal enrichment, so that the sintering machine is always caused to have the phenomenon of 'pasting grate bars', equipment corrosion is caused, and the safety production is influenced. The strong brine in industrial production contains a large amount of sodium chloride, the general treatment methods are slag flushing, coke quenching and zero discharge, slag flushing and coke quenching can generate substandard water slag due to extremely high salinity in the strong brine, and the zero discharge treatment is difficult to select preferentially due to the large equipment investment, no benefit and other factors.
At present, strong brine is used for replacing machine head ash water washing water, and zero emission of the strong brine is carried out while the sintering machine head ash is treated in a water washing and water washing liquid evaporation treatment mode, so that the problem that a large amount of machine head ash and strong brine cannot be treated in iron and steel enterprises can be effectively solved. According to the technology, valuable potassium salt, sodium salt and iron mud can be extracted, benefits are created from wastes, and sustainable development of enterprises is realized. In the process of multi-effect evaporation of the treated water, a small amount of non-condensable gas is generated, and a high concentration is formed by local long-term accumulation of the non-condensable gas on the condensation side, so that the heat transfer efficiency is obviously reduced. When the noncondensable gas is discharged through the noncondensable gas valve, if the concentration of the noncondensable gas at the discharge valve is low, the discharging process can be indelible, and the balance of the internal environment of evaporation is influenced, so that the precise discharge of the noncondensable gas in the multi-effect evaporator is required to be realized, and the noncondensable gas generated in the evaporation treatment of the aqueous solution is discharged on the basis of not influencing the evaporation performance.
Disclosure of Invention
The application provides a comprehensive utilization method and system of strong brine and sintering machine head ash, which are used for solving the problem of low heat exchange efficiency caused by untimely discharge of non-condensable gas in the multi-effect evaporation process of aqueous solution, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for comprehensive utilization of strong brine and sinter head ash, the method comprising the steps of:
sending the sintering machine head ash captured by the electric dust collector into an ash collecting bin by using a negative pressure pumping tank truck, and sequentially carrying out water washing, mud-water separation and impurity removal pretreatment on the sintering machine head ash by using a water washing tank to obtain an aqueous solution, and conveying the aqueous solution to a multi-effect evaporator by using a feeding pump;
determining an evaporation balance coefficient at each moment based on decomposition results of a temperature time sequence curve and a pressure time sequence curve in an evaporation window corresponding to each moment in the evaporation process of the multi-effect evaporator;
determining a temperature abnormal data sequence, a temperature abnormal time parameter sequence, a pressure abnormal data sequence and a pressure abnormal time parameter sequence of each monitoring window based on fitting curves of evaporation balance coefficients at all times;
determining the concentration of non-condensable gas aggregation of each evaporation area in each monitoring window based on a temperature anomaly data sequence, a temperature anomaly time parameter sequence, a pressure anomaly data sequence and a pressure anomaly time parameter sequence corresponding to each evaporation area in each monitoring window;
And determining a non-condensable gas emission decision based on the non-condensable gas concentration of all evaporation areas in the multi-effect evaporator in each monitoring window, finishing the evaporation treatment of the aqueous solution, and then carrying out salt extraction and drying on the aqueous solution after the evaporation treatment.
Preferably, the method for determining the evaporation balance coefficient at each moment based on the decomposition results of the temperature time sequence curve and the pressure time sequence curve in the corresponding evaporation window at each moment in the evaporation process of the multi-effect evaporator comprises the following steps:
determining an evaporation characteristic group of each evaporation area at each moment based on the evaporation temperature sequence and the evaporation pressure sequence of each evaporation area at each moment;
taking a matrix formed by evaporation characteristic groups of all evaporation areas at each moment according to the position sequence of the sensor as an evaporation characteristic matrix at each moment;
taking the evaporation characteristic matrixes at two adjacent moments as input, and acquiring two similarity scores of the two input evaporation characteristic matrixes by adopting an ANOSIM analysis method; taking the reciprocal of the sum of the product of the two similarity scores and a preset parameter as a first influence factor;
taking the accumulated results of the similarity measurement results between the evaporation characteristic groups of each evaporation area at each moment and the adjacent last moment on all the evaporation areas as second influence factors;
The evaporation balance coefficient at each moment consists of a first influence factor and a second influence factor, wherein the evaporation balance coefficient is in direct proportion to the first influence factor and the second influence factor respectively.
Preferably, the method for determining the evaporation characteristic group of each evaporation area at each moment based on the evaporation temperature sequence and the evaporation pressure sequence of each evaporation area at each moment comprises the following steps:
taking the monitoring range of each sensor in the multi-effect evaporator as an evaporation area, and taking the preset time length before each moment as an evaporation window of each moment;
respectively taking a sequence formed by all temperature data and pressure data in an evaporation window of each evaporation area at each moment according to a time sequence as an evaporation temperature sequence and an evaporation pressure sequence of each evaporation area at each moment;
respectively acquiring the evaporation temperature sequence of each evaporation area at each moment, the temperature trend item intensity and the pressure trend item intensity of each element in the evaporation pressure sequence by adopting a sequence decomposition algorithm;
and taking an array consisting of the temperature trend term intensity and the pressure trend term intensity of each element as an evaporation characteristic set.
Preferably, the method for determining the temperature anomaly data sequence, the temperature anomaly time sequence, the pressure anomaly data sequence and the pressure anomaly time sequence of each monitoring window based on the fitting curve of the evaporation balance coefficients at all the moments comprises the following steps:
determining an evaporation balance curve based on evaporation balance coefficients at all moments by adopting a nonlinear fitting algorithm;
taking the moment corresponding to each data point with the slope smaller than or equal to a preset threshold value on the evaporation balance curve as an evaporation balance moment; taking the subsequent preset time length of each evaporation balance moment as a monitoring window;
recording the moment that any one temperature value of each evaporation area in each monitoring window is smaller than the temperature value at the last moment as a temperature abnormality moment, taking the temperature data corresponding to each temperature abnormality moment as temperature abnormality data, and taking a sequence formed by all the temperature abnormality data of each evaporation area in each monitoring window according to a time sequence as a temperature abnormality data sequence of each evaporation area in each monitoring window;
recording the moment that any pressure value of each evaporation area in each monitoring window is larger than the temperature value at the previous moment as a pressure abnormality moment, taking temperature data corresponding to each pressure abnormality moment as pressure abnormality data, and taking a sequence formed by all the pressure abnormality data of each evaporation area in each monitoring window according to time sequence as a pressure abnormality data sequence of each evaporation area in each monitoring window;
And determining a temperature abnormality time parameter sequence and a pressure abnormality time parameter sequence of each evaporation area in each monitoring window based on the temperature abnormality time and the pressure abnormality time of each evaporation area in each monitoring window and the time length of the monitoring window.
Preferably, the method for determining the temperature abnormality time parameter sequence and the pressure abnormality time parameter sequence of each evaporation area in each monitoring window based on the temperature abnormality time and the pressure abnormality time of each evaporation area in each monitoring window and the time length of the monitoring window comprises the following steps:
taking the ratio of the time interval between each abnormal time and the starting time of each monitoring window to the time interval between the end time and the starting time of each monitoring window as an abnormal time parameter value of each abnormal time;
and respectively taking a sequence formed by the abnormal time parameter values corresponding to all the temperature abnormal time and the pressure abnormal time of each evaporation area in each monitoring window according to the time sequence as a temperature abnormal time parameter sequence and a pressure abnormal time parameter sequence of each evaporation area in each monitoring window.
Preferably, the method for determining the concentration of non-condensable gas aggregation in each monitoring window based on the temperature anomaly data sequence, the temperature anomaly time sequence, the pressure anomaly data sequence and the pressure anomaly time sequence corresponding to each evaporation region in each monitoring window comprises the following steps:
Determining an abnormality confidence factor of each evaporation area in each monitoring window based on the temperature abnormality data sequence and the pressure abnormality data sequence of each evaporation area in each monitoring window;
determining the non-condensable gas rarefaction factor of each evaporation area in each monitoring window based on the influence condition of the non-condensable gas in the multi-effect evaporator on the temperature and the pressure in the corresponding time period of each monitoring window;
taking all the temperature data and the pressure data of each evaporation area in each monitoring window as input, and acquiring LOF scores of the temperature data and the pressure data of each evaporation area in each monitoring window by adopting an LOF algorithm;
taking the sum of LOF scores corresponding to all elements in the temperature abnormal data sequence and the pressure abnormal data sequence of each evaporation area in each monitoring window as a first product factor;
the non-condensable gas concentration of each evaporation area in each monitoring window consists of an abnormal confidence factor, a non-condensable gas rarefaction factor and a first product factor, wherein the non-condensable gas concentration is in direct proportion to the abnormal confidence factor and the first product factor respectively, and the non-condensable gas concentration is in inverse proportion to the non-condensable gas rarefaction factor.
Preferably, the method for determining the abnormality confidence factor of each evaporation area in each monitoring window based on the temperature abnormality data sequence and the pressure abnormality data sequence of each evaporation area in each monitoring window comprises the following steps:
taking the inverse of the sum of the absolute value of the difference between the average value of all elements in the temperature abnormal moment parameter sequence of each evaporation area in each monitoring window and the average value of all elements in the pressure abnormal moment parameter sequence of each evaporation area in each monitoring window and the preset parameter as a first calculation factor;
taking the measurement results of element distribution in the temperature abnormal moment parameter sequence and the pressure abnormal moment parameter sequence of each evaporation area in each monitoring window as second calculation factors;
the abnormal confidence factor of each evaporation area in each monitoring window consists of a first calculation factor and a second calculation factor, wherein the abnormal confidence factors are respectively in direct proportion to the first calculation factor and the second calculation factor.
Preferably, the method for determining the non-condensable gas rarefaction factor of each evaporation area in each monitoring window based on the influence condition of the non-condensable gas in the multi-effect evaporator on the temperature and the pressure in the corresponding time period of each monitoring window comprises the following steps:
Taking the absolute value of the difference value between any one element of the temperature anomaly data sequence of each evaporation area in each monitoring window and the average value of all elements in the temperature anomaly data sequence as a first difference value, and taking the accumulation result of the sum of the first difference value and a preset parameter on the temperature anomaly data sequence of each evaporation area in each monitoring window as a first composition factor;
taking the absolute value of the difference value between any one element of the pressure abnormal data sequence of each evaporation area in each monitoring window and the average value of all elements in the pressure abnormal data sequence as a second difference value, and taking the accumulation result of the sum of the second difference value and preset parameters on the pressure abnormal data sequence of each evaporation area in each monitoring window as a second composition factor;
the reciprocal of the product of the first composition factor and the second composition factor is taken as the noncondensable gas rarefaction factor of each evaporation area in each monitoring window.
Preferably, the method for determining the discharge decision of the noncondensable gas based on the concentration of the noncondensable gas in each monitoring window of all evaporation areas in the multi-effect evaporator comprises the following steps:
taking the concentration of non-condensable gas aggregation, the average value of temperature data and the average value of pressure data of all evaporation areas under the same monitoring window as input; determining the comprehensive evaluation score of each evaporation area in each monitoring window by adopting a TOPSIS algorithm;
Taking the comprehensive evaluation scores of all the evaporation areas under each monitoring window as input, and acquiring the upper quartile of all the comprehensive evaluation scores by adopting a quartile method;
and taking the difference value between the ratio of the number of the evaporation areas with the comprehensive evaluation score larger than the upper quartile to the total number of the evaporation areas and the preset threshold value under each monitoring window as an exhaust decision value of each monitoring window.
In a second aspect, embodiments of the present application further provide a comprehensive utilization system of strong brine and sinter head ash, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when the processor executes the computer program.
The beneficial effects of this application are: according to the method, the evaporation balance time in the evaporation process of the aqueous solution is determined according to the stability of pressure and temperature data in evaporation areas at different positions at each moment, so that the non-condensable gas analysis generated by the evaporation of the aqueous solution in the subsequent multi-effect evaporator can be ensured to be performed on the basis of stable evaporation treatment of the aqueous solution; secondly, determining a non-condensable gas rarefaction factor by analyzing the fluctuation degree of data in temperature abnormal data sequences and pressure abnormal data sequences in different evaporation areas in the corresponding time period of the same monitoring window; the stability degree of the non-condensable gas state is evaluated based on the concentration degree of the temperature abnormal moment and the pressure abnormal moment, and an abnormal confidence factor is determined; determining the concentration of non-condensable gas aggregation through the abnormal confidence factor and the non-condensable gas rarefaction factor; determining an exhaust decision value for each monitoring window based on the concentration of non-condensable gas in all evaporation areas under each monitoring window; the device has the beneficial effects that the discharge of noncondensable gas in the evaporation area can be controlled in real time, the heat transfer efficiency in the evaporation process of the aqueous solution is improved, the aqueous solution is better evaporated on the basis of ensuring the performance of evaporation equipment, and the comprehensive utilization efficiency of strong brine and sintering machine head ash is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for comprehensive utilization of strong brine and sinter head ash according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a comprehensive utilization system according to an embodiment of the present application;
FIG. 3 is a flow chart of an implementation of determining an evaporation balance coefficient according to one embodiment of the present application;
FIG. 4 is a flow chart of an implementation of determining concentration of non-condensable gas accumulation according to one embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Referring to fig. 1, a flowchart of a method for comprehensive utilization of strong brine and sinter head ash according to an embodiment of the present application is shown, the method includes the following steps:
and S001, determining a treatment flow of comprehensive utilization of the strong brine and the sintering machine head ash, and collecting temperature and pressure data at each moment.
The comprehensive utilization of the strong brine and the sintering machine head ash in the application refers to the extraction of sodium salt and potassium salt by the strong brine and the sintering machine head ash, and the whole process of the comprehensive utilization comprises the following steps: the comprehensive utilization system comprises a collecting module, a water washing module, a separating module, a impurity removing module, an evaporating module and a salt extracting module, wherein each module corresponds to each processing step in sequence. The processing flow of each step is as follows:
s1, collecting raw materials: the sintering machine head ash captured by the electric dust collector is sent into an ash collecting bin by using a negative pressure pumping tank truck, the bin is required to be of a fully-sealed structure, an air hammer device is required to be arranged in the bin, the phenomenon that the machine head ash is agglomerated due to the action of gravity in the blanking process is prevented from blocking a blanking hole, and the dust collector is arranged in the blanking process, so that dust can not be dissipated in the operation treatment in the ash storage and blanking stages; the main components in the collected sintering machine head ash and the percentages are: 6.69% of total iron, 43.17% of potassium oxide, 4.28% of sodium oxide, 3.98% of calcium oxide, 2.5% of sulfur, 0.2% of zinc and 33.27% of chlorine; the relevant parameters of strong brine used in this application are PH:7.0 11、/>71400 mg/L->12000mg/L and calcium hardness->300mg/L, a conductivity of 130000 +.>；
S2, washing: sending the sintering machine head ash at the feed opening into a water washing tank, and preventing the manufacturing materials of the water washing stirrer from corrosion, so that safety accidents caused by corrosion of strong brine on the stirring device in long-term use are avoided. Fully mixing strong brine with sintering machine head ash in a water washing pool to generate a mixture, keeping the mixture mixed for a certain time, dissolving potassium, sodium, chlorine and other ions in the ash in water, and promoting the combination of chloride ions and metal ions to generate salt;
s3, mud-water separation: the mixture in the steps is sent into a filter pressing system in a pump lifting mode, solid-liquid separation is realized through the filter pressing system, filtered dehydrated sludge can be recycled by selecting a sintering stock ground, and the dehydrated sludge can also be optionally sent into a water washing tank again for secondary or even tertiary water washing. The filtrate enters the next working section for treatment. The filter press that uses among the filter-pressing system in this application is the plate-and-frame filter press, and the reason is that this kind of equipment simple structure, simple operation, filtration area selection range are nimble, and the occupation of land is few, and the operation is stable, and it is convenient to maintain, goes out mud moisture content and is not higher than 20%. Secondly, the baffle and the cover plate are arranged in the water washing tank and the conveyor, so that dust dissipation phenomenon is avoided in the process that the sintering machine head ash is conveyed to the water washing tank and mixed and stirred;
S4, impurity removal pretreatment: delivering the obtained filter-pressing liquid effluent into a high-efficiency sedimentation tank to remove suspended matters and hard matters; the effluent of the sedimentation tank enters a two-stage post-treatment system and is used for ensuring that the condition that a pipeline is blocked by sludge does not occur in the operation of subsequent equipment;
s5, multi-effect evaporation: and (3) conveying the sediment-removed water obtained in the steps to a condensed water heat exchanger by a feeding pump, then entering a first-effect evaporator of a potassium evaporation system, and heating and concentrating by steam. Heating and then entering a two-effect evaporator, and further concentrating on the basis of heating by one-effect steam until the material crystallization concentration is reached, wherein at the moment, potassium chloride crystals are separated out and sodium chloride is not separated out due to different solubilities of the potassium chloride and the sodium chloride; in the application, a multi-effect evaporation method is adopted, and the secondary steam of the previous effect is used as the series evaporation operation of the heating steam of the next effect. In the multi-effect evaporation, the operating pressure, the corresponding heating steam temperature and the boiling point of the solution are sequentially reduced, the steam resources are utilized to the maximum extent, and meanwhile, the materials can automatically flow into the next working procedure;
s6, salt drying and extraction: and separating out the corresponding solid wet salt by using a centrifugal machine, and drying and packaging.
The application aims to improve the heat transfer efficiency in the evaporation device by controlling the discharge of noncondensable gas generated during the evaporation of the aqueous solution in the multi-effect evaporation stage, so that the aqueous solution is better evaporated on the basis of ensuring the performance of evaporation equipment. Therefore, the temperature and the pressure in the evaporation process in each evaporator are considered to be collected.
Specifically, the temperature sensor and the pressure sensor are respectively installed on the inner wall of the device of each multi-effect evaporator, the number of the sensors between two adjacent rows or two adjacent columns is consistent, the sensors are numbered according to the rules from left to right and from low to high, the number of the sensors depends on the size of the area in the multi-effect evaporator, and the monitoring range of each sensor can cover the area in the whole multi-effect evaporator, namely, each sensor can monitor one evaporation area in the multi-effect evaporator. The time interval between two adjacent data acquisitions of each sensor is set to 0.5s. In order to avoid abnormal data caused by noise interference or abnormal sensor transmission in the data acquisition process, data cleaning is performed on temperature and pressure data acquired by each sensor, and the data cleaning is a known technology, and detailed processes are not repeated.
So far, the temperature and pressure data of each sensor at each moment are obtained respectively and are used for calculating the evaporation balance coefficient at each moment subsequently.
Step S002, determining the evaporation balance coefficient at each moment based on the decomposition results of the temperature time sequence curve and the pressure time sequence curve in the corresponding evaporation window at each moment.
In the multi-effect evaporation treatment stage of the precipitation removal aqueous solution, the temperature gradually rises until the temperature is stable; the pressure will gradually decrease until it stabilizes. When the evaporation treatment of the aqueous solution tends to be stable, as the aqueous solution contains solute with higher concentration, the heat transfer efficiency between the solution and steam is reduced, so that noncondensable gas is generated, as the concentration volume of the noncondensable gas is increased, an aggregation phenomenon is generated between the noncondensable gas, and as the noncondensable gas clusters reduce the effective evaporation area of the position where the noncondensable gas clusters are located, the heat transfer efficiency is reduced, so that the temperature of the position where the noncondensable gas clusters are located is reduced; the non-condensable gas releases heat during the condensation process, and the heat can enable surrounding steam molecules to obtain energy, so that the steam molecules are easier to become gas, and the steam pressure is increased.
Based on the above analysis, as shown in fig. 3, the present application considers whether the evaporation state of the precipitation removal aqueous solution at each moment is in an equilibrium state or not by analyzing the trend of the data in the inner wall temperature matrix and the pressure monitoring matrix at adjacent moments.
Specifically, in one embodiment, the monitoring range of each sensor is taken as an evaporation area, the first t minutes of each moment is taken as an evaporation window of each moment, and the magnitude of t takes an empirical value of 1. Taking a sequence formed by all temperature data of each evaporation area in an evaporation window at each moment according to time sequence as an evaporation temperature sequence of each evaporation area at each moment; the sequence of all pressure data in the evaporation window of each evaporation area at each moment in time is taken as the evaporation pressure sequence of each evaporation area at each moment in time.
Next, taking the a-th evaporation area as an example, the evaporation temperature sequences at the i-th moment of the a-th evaporation area are respectively calculatedEvaporation pressure sequence->As input, the evaporation temperature sequence +.A STL (Seasonal and Trend decomposition using Loess) algorithm was used to obtain>Evaporation pressure sequence->The STL algorithm is a known technology, and the specific process is not repeated. Second, the evaporation temperature sequence will be +.>Evaporation pressure sequence->The array of trend term intensities at each instant in time is used as the evaporation characteristic set at each instant in the a-th evaporation area. For example, the sequence of evaporation temperatures will be +.>Pressure of evaporationSequence->An array of trend term intensities at the j-th moment of the a-th evaporation zone as an evaporation feature set +.>Wherein->、/>Respectively vaporization temperature sequence->Evaporation pressure sequence->Trend term intensity at the j-th moment in the (b).
Further, according to the steps, evaporation characteristic groups of all evaporation areas at each moment are obtained respectively, and a matrix formed by the evaporation characteristic groups of all the evaporation areas at each moment according to the position sequence of the sensor is used as an evaporation characteristic matrix at each moment. Taking the j-th moment and the j-1-th moment as examples of evaporation feature matrixes at any two moments, obtaining similarity scores p and q between the two matrixes by adopting a ANOSIM (Analysis of Similarities) analysis method, wherein the larger the value of p is, the more obvious the difference between the two input matrixes is, the larger the value of q is, which indicates that the larger the difference between column vectors with the same sequence of the two evaporation feature moments is, and the ANOSIM analysis method is a known technology, and the specific process is not repeated.
Based on the above analysis, an evaporation balance coefficient is constructed here to characterize whether the evaporation process of the aqueous solution in the multiple effect evaporator tends to an equilibrium state at each moment. Calculating the evaporation balance coefficient at the j-th moment:
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in the method, in the process of the invention,is the evaporation equilibrium coefficient at the j-th moment, < >>Is a normalization function->、/>The similarity score values of the evaporation characteristic matrixes at the j th moment and the j-1 th moment are obtained by adopting an ANOSIM analysis method respectively, n is the number of evaporation areas in the multi-effect evaporator, and>、/>the evaporation characteristic groups of the a evaporation area at the j th and j-1 th moments are respectively +.>Is->、/>Cosine similarity between->Is a parameter regulating factor for preventing denominator from being 0, & lt/L>The size of (2) is 0.001.
Wherein the evaporation treatment of the water solution removed by precipitation at two adjacent moments is closer to the equilibrium state, the sizes of the elements on the same sequence of the evaporation characteristic matrix at the j th moment and the j-1 th moment are closer,the smaller the value of (2), the smaller the difference between the evaporation characteristic matrices at the j-th and j-1-th moments, +.>The smaller the value of (2), the first influencing factor +.>The greater the value of (2); the more stable the evaporation treatment of the sediment-removed aqueous solution at the j-th moment, the less the variation degree of the trend item intensity of the temperature and pressure data in the evaporation temperature sequence, the j-1-th moment, and the evaporation pressure sequence, the smaller the variation degree of the elements in the evaporation characteristic groups of each evaporation area at the j-th moment and the j-1-th moment in the multi-effect evaporator, the higher the similarity degree between the evaporation characteristic groups >The larger the value of (2) the second influencing factor +.>The greater the value of (2).
So far, the evaporation balance coefficient of each moment in the evaporation process of the sediment removal aqueous solution is obtained and is used for determining the non-condensing concentration in the multi-effect evaporator.
And step S003, determining the concentration of non-condensable gas aggregation based on the abnormal conditions of the temperature and pressure data analyzed by the evaporation balance coefficient and the abnormal moment.
In the evaporation process of the precipitation removal aqueous solution, along with the change of evaporation time, temperature and pressure in the multi-effect evaporator, the evaporation state of the precipitation removal aqueous solution is in a continuous change state, so that the aggregation degree of non-condensable gas in the multi-effect reactor is also changed continuously, and in order to ensure that the evaporation efficiency of the precipitation removal aqueous solution is higher, the non-condensable gas needs to be removed in time. In the application, considering the relationship between the fluctuation condition of temperature and pressure data analyzed based on the evaporation balance coefficient and the noncondensable gas, the concentration of noncondensable gas aggregation in each evaporation area in each monitoring window is analyzed, and the whole flow is shown in fig. 4.
Specifically, in one embodiment, each moment and the evaporation balance coefficient of each moment are input, a least square fitting algorithm is adopted to obtain an evaporation balance curve, the abscissa of the evaporation balance curve is time, the ordinate is the evaporation balance coefficient, and the least square fitting algorithm is a known technology, and the specific process is not repeated. In another embodiment, the input is the evaporation balance coefficient at each time and each time, and a polynomial fitting algorithm may be further used to obtain an evaporation balance curve, where the abscissa of the evaporation balance curve is time and the ordinate is the evaporation balance coefficient, and the polynomial fitting algorithm is a well-known technology, and the specific process is not repeated. Secondly, acquiring the slope of each data point on the evaporation balance curve, recording the moment corresponding to the data point with the slope smaller than or equal to the slope threshold as an evaporation balance moment, and taking the checked value of the slope threshold as 0.3.
During evaporation of the aqueous solution from which precipitation is to be removed, noncondensable gases are usually produced after the aqueous solution evaporation process has stabilized. For any evaporation balance time, taking t minutes after each evaporation balance time as a monitoring window, and taking an empirical value of 1 for the size of t. In a monitoring window, the more obvious the temperature drop and the more obvious the pressure rise of the a-th evaporation area are, the higher the aggregation degree of the noncondensable gas generated in the a-th evaporation area in the time period corresponding to the monitoring window is, the noncondensable gas exhaust valve should be opened in time to discharge the noncondensable gas, so that the noncondensable gas is prevented from affecting the evaporation treatment of the aqueous solution.
Taking the g-th monitoring window as an example for any one of the monitoring windows, in the g-th monitoring window, recording the moment when any one of the temperature values in the a-th evaporation area is smaller than the temperature value at the last moment as a temperature abnormality moment, and taking the temperature data corresponding to each temperature abnormality moment as temperature abnormality data; and (3) recording the moment when any pressure value in the a-th evaporation area is larger than the pressure value at the last moment as a pressure abnormality moment, and taking the pressure data corresponding to each pressure abnormality moment as pressure abnormality data. In the g-th monitoring window, the sequence formed by all the temperature abnormal data and the pressure abnormal data in the a-th evaporation area according to the time sequence is respectively marked as a temperature abnormal data sequence and a pressure abnormal data sequence of the a-th evaporation area in the g-th monitoring window.
Secondly, regarding all abnormal temperature moments and abnormal pressure moments in the a-th evaporation area, carrying out dimensionality removal processing on each abnormal moment in order to facilitate subsequent calculation. And taking the ratio of the time interval between each abnormal time and the starting time of the g-th monitoring window to the time interval between the end time and the starting time of the g-th monitoring window as an abnormal time parameter value of each abnormal time. And respectively marking a sequence formed by the abnormal time parameter values corresponding to all the temperature abnormal time and the pressure abnormal time in the a-th evaporation area according to the time sequence as a temperature abnormal time parameter sequence and a pressure abnormal time parameter sequence of the a-th evaporation area in the g-th monitoring window.
Further, temperature data and pressure data of the a-th evaporation area in the g-th monitoring window are respectively taken as input, an LOF (Local Outlier Factor) algorithm is adopted to respectively obtain LOF scores of each temperature data and pressure data of the a-th evaporation area in the g-th monitoring window, and the LOF algorithm is a known technology and a specific process is not repeated.
Based on the above analysis, a concentration of non-condensable gas is constructed here to characterize the concentration of non-condensable gas in each evaporation zone over a corresponding time period for each monitoring window. Calculating the concentration of non-condensable gas in the evaporation area a in the monitoring window g: ；
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In the method, in the process of the invention,is the abnormal confidence factor of the a evaporation area in the g monitoring window,/for the evaporation area>、/>The temperature abnormality time parameter sequence and the pressure abnormality time parameter sequence of the a-th evaporation area in the g-th monitoring window are respectively +.>、Respectively the sequences->、/>Mean value of the elements>Is a parameter regulating factor for preventing denominator from being 0, & lt/L>The size of (2) is 0.001,/v>As a Jacquard coefficient function, +.>For calculating the sequence +.>、/>The calculation of the Jacquard coefficient is a known technology, and the specific process is not repeated;
is the noncondensable gas rarefaction factor of the a evaporation area in the g monitoring window,>、/>the number of elements in the temperature abnormal data sequence and the pressure abnormal data sequence of the a-th evaporation area in the g-th monitoring window are respectively;、/>the (a) th evaporation area is the (x) th temperature anomaly data and the (x) th evaporation area in the (g) th monitoring window>Pressure abnormality data->、/>The average values of elements in the temperature abnormal data sequence and the pressure abnormal data sequence of the a-th evaporation area in the g-th monitoring window are respectively;
is the concentration of non-condensable gas in the evaporation area a in the monitoring window g,/>Is the cumulative sum of LOF scores of all temperature anomaly data of the a-th evaporation zone in the g-th monitoring window, >Is the accumulated sum of LOF scores of all pressure anomaly data of the a-th evaporation area in the g-th monitoring window, wherein L, k means a sequence consisting of the sequence value of the temperature anomaly data and the sequence value of the pressure anomaly data of the a-th evaporation area in the g-th monitoring window respectively,wherein->、/>、/>The 1 st, 2 nd and +.>Sequence values of the individual temperature anomaly data;wherein->，/>，/>The 1 st, 2 nd and +.>The order value of the individual pressure anomaly data.
Wherein the more concentrated the time of the temperature drop and the pressure rise of the a-th evaporation area in the g-th monitoring window, the closer the sizes of elements in the temperature abnormality time parameter sequence and the pressure abnormality time parameter sequence are,the smaller the value of (2), the first calculation factor +.>The greater the value of (a) the greater the likelihood that the abnormality in temperature and pressure of the (a) th evaporation zone in the (g) th monitoring window is caused by the generation of noncondensable gasThe more similar the distribution of temperature anomaly moment and pressure anomaly moment are, the second calculation factor +.>The greater the value of +.>The greater the value of (2); the thinner the noncondensable gas generated by the a-th evaporation area in the g-th monitoring window, the lighter the temperature drop caused by the noncondensable gas generation in the g-th monitoring window, the closer the element sizes of the a-th evaporation area in the temperature anomaly data sequence in the g-th monitoring window are, the first difference value The smaller the value of (1) the first composition factor +.>The smaller the value of (2); the slight pressure rise caused by the generation of noncondensable gas in the g-th monitoring window is that the element sizes of the a-th evaporation area in the pressure anomaly data sequence in the g-th monitoring window are close to each other, the second difference is->The smaller the value of (2) the second composition factorThe smaller the value of +.>The greater the value of (2); the more obvious the temperature abnormality and pressure abnormality caused by the generation of noncondensable gas, the higher the abnormality degree of the temperature abnormality data and the pressure abnormality data of the a-th evaporation area in the g-th monitoring window, the first product factor->The greater the value of (2); i.e. < ->The greater the value of (a) the evaporation zone is within the period corresponding to the g-th monitoring windowThe greater the concentration of non-condensable gas produced.
Thus, the concentration of the non-condensable gas aggregate of each evaporation area in each monitoring window is obtained and is used for the subsequent non-condensable gas emission decision.
And S004, determining a non-condensable gas emission decision based on the non-condensable gas concentration of all evaporation areas in the multi-effect evaporator in each monitoring window, completing the evaporation treatment of the aqueous solution, and then carrying out salt extraction and drying on the aqueous solution after the evaporation treatment.
According to the steps, after the non-condensable gas aggregation concentration of each evaporation area in each monitoring window is obtained, whether the non-condensable gas generated in the corresponding time period of each monitoring window reaches the emission requirement or not needs to be evaluated for the non-condensable gas aggregation concentration of all evaporation areas in the whole multi-effect reactor in each monitoring window.
Specifically, the concentration of non-condensable gas aggregation in each monitoring window of all evaporation areas in the multi-effect evaporator is obtained respectively. Secondly, taking the non-condensable gas concentration, the average value of temperature data and the average value of pressure data of all evaporation areas under the same monitoring window as input, and acquiring a comprehensive evaluation score of each evaporation area in each monitoring window by adopting a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm, wherein the weights of the three input data of the non-condensable gas concentration, the average value of the temperature data and the average value of the pressure data are determined by an entropy weight method, specifically, taking the non-condensable gas concentration, the average value of the temperature data and the average value of the pressure data of all evaporation areas under each monitoring window as input, and determining the weight of each input by adopting the entropy weight method, wherein the TOPSIS algorithm and the entropy weight method are known techniques, and specific processes are not repeated.
Further, for any one monitoring window, the comprehensive evaluation score of all the evaporation areas under each monitoring window is taken as input, and the upper quartile of all the comprehensive evaluation scores is obtained by adopting a quartile method, wherein the quartile is a known technology, and the specific process is not repeated. Determining an exhaust decision value for each monitoring window based on the upper quartile of the composite evaluation score:
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In the method, in the process of the invention,is the exhaust decision value of the g-th monitoring window, < >>Is the number of all evaporation areas with the comprehensive evaluation score larger than the upper quartile under the g-th monitoring window, and n is the total number of evaporation areas in the multi-effect evaporator, ++>Is the decision threshold value and,the size takes the checked value of 0.3.
Exhaust decision value at g-th monitoring windowAnd if the concentration of the non-condensable gas is larger than 0, the concentration of the non-condensable gas in the corresponding time period of the g monitoring window is considered to reach the emission requirement, and according to the exhaust area in the multi-effect evaporation pseudo-color image, the non-condensable gas valve in the exhaust area in the multi-effect evaporator is opened, so that the generated non-condensable gas in the evaporation treatment process of the aqueous solution is discharged, and the heat transfer efficiency is improved.
According to the mode, noncondensable gas affecting the evaporation treatment of the aqueous solution can be discharged in real time based on the monitoring window, so that the evaporation efficiency is improved, the evaporation device is protected, the evaporation treatment of the aqueous solution is realized, and further, salt is obtained through the subsequent process treatment:
salt extraction and drying: the method comprises the steps that slurry in an effect body is evaporated to separate out salt particles, the slurry with the salt particles enters a centrifugal machine through a cyclone for centrifugal separation due to the fact that gravity is settled to the bottom of the effect body, the cyclone has a certain centrifugal effect, the solid content can be further improved, potassium chloride solid wet salt separated by the centrifugal machine is sent to a drying module in a comprehensive utilization system for drying, and after being detected to be qualified by a laboratory, the potassium chloride solid wet salt is packed into bags and sent to a warehouse, and drying module equipment for drying the potassium chloride solid wet salt in the application is a tray dryer; mother liquor after centrifugal dehydration enters a sodium chloride evaporation system, a matched vacuum pump unit can control the atmospheric pressure in an effective body so as to ensure that slurry can still be boiled, evaporated and crystallized at different temperatures, and solid materials after crystallization are separated and dehydrated by a centrifugal machine and then are dried and packaged by a drying module in a comprehensive utilization system.
So far, the potassium salt and the sodium salt can be obtained by comprehensively utilizing the strong brine and the sintering machine head ash through all the steps.
Based on the same inventive concept as the above method, the embodiment of the application further provides a comprehensive utilization system of strong brine and sintering machine head ash, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of any one of the above comprehensive utilization methods of the strong brine and the sintering machine head ash.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present application are intended to be included within the scope of the present application.
Claims (10)
1. The comprehensive utilization method of the strong brine and the sintering machine head ash is characterized by comprising the following steps of:
Sending the sintering machine head ash captured by the electric dust collector into an ash collecting bin by using a negative pressure pumping tank truck, and sequentially carrying out water washing, mud-water separation and impurity removal pretreatment on the sintering machine head ash by using a water washing tank to obtain an aqueous solution, and conveying the aqueous solution to a multi-effect evaporator by using a feeding pump;
determining an evaporation balance coefficient at each moment based on decomposition results of a temperature time sequence curve and a pressure time sequence curve in an evaporation window corresponding to each moment in the evaporation process of the multi-effect evaporator;
determining a temperature abnormal data sequence, a temperature abnormal time parameter sequence, a pressure abnormal data sequence and a pressure abnormal time parameter sequence of each monitoring window based on fitting curves of evaporation balance coefficients at all times;
determining the concentration of non-condensable gas aggregation of each evaporation area in each monitoring window based on a temperature anomaly data sequence, a temperature anomaly time parameter sequence, a pressure anomaly data sequence and a pressure anomaly time parameter sequence corresponding to each evaporation area in each monitoring window;
and determining a non-condensable gas emission decision based on the non-condensable gas concentration of all evaporation areas in the multi-effect evaporator in each monitoring window, finishing the evaporation treatment of the aqueous solution, and then carrying out salt extraction and drying on the aqueous solution after the evaporation treatment.
2. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 1, wherein the method for determining the evaporation balance coefficient at each moment based on the decomposition result of the temperature time sequence curve and the pressure time sequence curve in the corresponding evaporation window at each moment in the evaporation process of the multi-effect evaporator is as follows:
determining an evaporation characteristic group of each evaporation area at each moment based on the evaporation temperature sequence and the evaporation pressure sequence of each evaporation area at each moment;
taking a matrix formed by evaporation characteristic groups of all evaporation areas at each moment according to the position sequence of the sensor as an evaporation characteristic matrix at each moment;
taking the evaporation characteristic matrixes at two adjacent moments as input, and acquiring two similarity scores of the two input evaporation characteristic matrixes by adopting an ANOSIM analysis method; taking the reciprocal of the sum of the product of the two similarity scores and a preset parameter as a first influence factor;
taking the accumulated results of the similarity measurement results between the evaporation characteristic groups of each evaporation area at each moment and the adjacent last moment on all the evaporation areas as second influence factors;
the evaporation balance coefficient at each moment consists of a first influence factor and a second influence factor, wherein the evaporation balance coefficient is in direct proportion to the first influence factor and the second influence factor respectively.
3. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 2, wherein the method for determining the evaporation characteristic group of each evaporation area at each moment based on the evaporation temperature sequence and the evaporation pressure sequence of each evaporation area at each moment is as follows:
taking the monitoring range of each sensor in the multi-effect evaporator as an evaporation area, and taking the preset time length before each moment as an evaporation window of each moment;
respectively taking a sequence formed by all temperature data and pressure data in an evaporation window of each evaporation area at each moment according to a time sequence as an evaporation temperature sequence and an evaporation pressure sequence of each evaporation area at each moment;
respectively acquiring the evaporation temperature sequence of each evaporation area at each moment, the temperature trend item intensity and the pressure trend item intensity of each element in the evaporation pressure sequence by adopting a sequence decomposition algorithm;
and taking an array consisting of the temperature trend term intensity and the pressure trend term intensity of each element as an evaporation characteristic set.
4. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 1, wherein the method for determining the temperature anomaly data sequence, the temperature anomaly time sequence, the pressure anomaly data sequence and the pressure anomaly time sequence of each monitoring window based on the fitting curve of the evaporation balance coefficients at all the moments is as follows:
Determining an evaporation balance curve based on evaporation balance coefficients at all moments by adopting a nonlinear fitting algorithm;
taking the moment corresponding to each data point with the slope smaller than or equal to a preset threshold value on the evaporation balance curve as an evaporation balance moment; taking the subsequent preset time length of each evaporation balance moment as a monitoring window;
recording the moment that any one temperature value of each evaporation area in each monitoring window is smaller than the temperature value at the last moment as a temperature abnormality moment, taking the temperature data corresponding to each temperature abnormality moment as temperature abnormality data, and taking a sequence formed by all the temperature abnormality data of each evaporation area in each monitoring window according to a time sequence as a temperature abnormality data sequence of each evaporation area in each monitoring window;
recording the moment that any pressure value of each evaporation area in each monitoring window is larger than the temperature value at the previous moment as a pressure abnormality moment, taking temperature data corresponding to each pressure abnormality moment as pressure abnormality data, and taking a sequence formed by all the pressure abnormality data of each evaporation area in each monitoring window according to time sequence as a pressure abnormality data sequence of each evaporation area in each monitoring window;
And determining a temperature abnormality time parameter sequence and a pressure abnormality time parameter sequence of each evaporation area in each monitoring window based on the temperature abnormality time and the pressure abnormality time of each evaporation area in each monitoring window and the time length of the monitoring window.
5. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 4, wherein the method for determining the temperature abnormality time parameter sequence and the pressure abnormality time parameter sequence of each evaporation area in each monitoring window based on the temperature abnormality time and the pressure abnormality time of each evaporation area in each monitoring window and the time length of the monitoring window is as follows:
taking the ratio of the time interval between each abnormal time and the starting time of each monitoring window to the time interval between the end time and the starting time of each monitoring window as an abnormal time parameter value of each abnormal time;
and respectively taking a sequence formed by the abnormal time parameter values corresponding to all the temperature abnormal time and the pressure abnormal time of each evaporation area in each monitoring window according to the time sequence as a temperature abnormal time parameter sequence and a pressure abnormal time parameter sequence of each evaporation area in each monitoring window.
6. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 1, wherein the method for determining the concentration of non-condensable gas aggregation of each evaporation area in each monitoring window based on the temperature anomaly data sequence, the temperature anomaly time sequence, the pressure anomaly data sequence and the pressure anomaly time sequence corresponding to each evaporation area in each monitoring window is as follows:
determining an abnormality confidence factor of each evaporation area in each monitoring window based on the temperature abnormality data sequence and the pressure abnormality data sequence of each evaporation area in each monitoring window;
determining the non-condensable gas rarefaction factor of each evaporation area in each monitoring window based on the influence condition of the non-condensable gas in the multi-effect evaporator on the temperature and the pressure in the corresponding time period of each monitoring window;
taking all the temperature data and the pressure data of each evaporation area in each monitoring window as input, and acquiring LOF scores of the temperature data and the pressure data of each evaporation area in each monitoring window by adopting an LOF algorithm;
taking the sum of LOF scores corresponding to all elements in the temperature abnormal data sequence and the pressure abnormal data sequence of each evaporation area in each monitoring window as a first product factor;
The non-condensable gas concentration of each evaporation area in each monitoring window consists of an abnormal confidence factor, a non-condensable gas rarefaction factor and a first product factor, wherein the non-condensable gas concentration is in direct proportion to the abnormal confidence factor and the first product factor respectively, and the non-condensable gas concentration is in inverse proportion to the non-condensable gas rarefaction factor.
7. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 6, wherein the method for determining the abnormal confidence factor of each evaporation area in each monitoring window based on the temperature abnormal data sequence and the pressure abnormal data sequence of each evaporation area in each monitoring window is as follows:
taking the inverse of the sum of the absolute value of the difference between the average value of all elements in the temperature abnormal moment parameter sequence of each evaporation area in each monitoring window and the average value of all elements in the pressure abnormal moment parameter sequence of each evaporation area in each monitoring window and the preset parameter as a first calculation factor;
taking the measurement results of element distribution in the temperature abnormal moment parameter sequence and the pressure abnormal moment parameter sequence of each evaporation area in each monitoring window as second calculation factors;
The abnormal confidence factor of each evaporation area in each monitoring window consists of a first calculation factor and a second calculation factor, wherein the abnormal confidence factors are respectively in direct proportion to the first calculation factor and the second calculation factor.
8. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 6, wherein the method for determining the non-condensable gas rarefaction factor of each evaporation area in each monitoring window based on the influence condition of the non-condensable gas in the multi-effect evaporator on temperature and pressure in the corresponding time period of each monitoring window is as follows:
taking the absolute value of the difference value between any one element of the temperature anomaly data sequence of each evaporation area in each monitoring window and the average value of all elements in the temperature anomaly data sequence as a first difference value, and taking the accumulation result of the sum of the first difference value and a preset parameter on the temperature anomaly data sequence of each evaporation area in each monitoring window as a first composition factor;
taking the absolute value of the difference value between any one element of the pressure abnormal data sequence of each evaporation area in each monitoring window and the average value of all elements in the pressure abnormal data sequence as a second difference value, and taking the accumulation result of the sum of the second difference value and preset parameters on the pressure abnormal data sequence of each evaporation area in each monitoring window as a second composition factor;
The reciprocal of the product of the first composition factor and the second composition factor is taken as the noncondensable gas rarefaction factor of each evaporation area in each monitoring window.
9. The method for comprehensively utilizing strong brine and sintering machine head ash according to claim 1, wherein the method for determining the discharge decision of the non-condensable gas based on the non-condensable gas aggregation concentration of all evaporation areas in the multi-effect evaporator in each monitoring window is as follows:
taking the concentration of non-condensable gas aggregation, the average value of temperature data and the average value of pressure data of all evaporation areas under the same monitoring window as input; determining the comprehensive evaluation score of each evaporation area in each monitoring window by adopting a TOPSIS algorithm;
taking the comprehensive evaluation scores of all the evaporation areas under each monitoring window as input, and acquiring the upper quartile of all the comprehensive evaluation scores by adopting a quartile method;
and taking the difference value between the ratio of the number of the evaporation areas with the comprehensive evaluation score larger than the upper quartile to the total number of the evaporation areas and the preset threshold value under each monitoring window as an exhaust decision value of each monitoring window.
10. A system for the integrated utilization of strong brine and sinter head ash, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor, when executing the computer program, realizes the steps of a method for the integrated utilization of strong brine and sinter head ash according to any one of claims 1-9.
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