CN112044270A - Desulfurization oxidation air system control method based on data-driven multiple models - Google Patents

Desulfurization oxidation air system control method based on data-driven multiple models Download PDF

Info

Publication number
CN112044270A
CN112044270A CN202010828597.4A CN202010828597A CN112044270A CN 112044270 A CN112044270 A CN 112044270A CN 202010828597 A CN202010828597 A CN 202010828597A CN 112044270 A CN112044270 A CN 112044270A
Authority
CN
China
Prior art keywords
caso
data
absorption tower
concentration
oxidation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010828597.4A
Other languages
Chinese (zh)
Other versions
CN112044270B (en
Inventor
杨艳春
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guoneng Shandong Energy Environment Co ltd
Guoneng Longyuan Environmental Protection Co Ltd
Original Assignee
Beijing Guodian Longyuan Environmental Engineering Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Guodian Longyuan Environmental Engineering Co Ltd filed Critical Beijing Guodian Longyuan Environmental Engineering Co Ltd
Priority to CN202010828597.4A priority Critical patent/CN112044270B/en
Publication of CN112044270A publication Critical patent/CN112044270A/en
Application granted granted Critical
Publication of CN112044270B publication Critical patent/CN112044270B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/346—Controlling the process
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/46—Removing components of defined structure
    • B01D53/48—Sulfur compounds
    • B01D53/50—Sulfur oxides
    • B01D53/501—Sulfur oxides by treating the gases with a solution or a suspension of an alkali or earth-alkali or ammonium compound
    • B01D53/502—Sulfur oxides by treating the gases with a solution or a suspension of an alkali or earth-alkali or ammonium compound characterised by a specific solution or suspension
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
    • B01D53/80—Semi-solid phase processes, i.e. by using slurries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/96—Regeneration, reactivation or recycling of reactants
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D2251/00—Reactants
    • B01D2251/40—Alkaline earth metal or magnesium compounds
    • B01D2251/404—Alkaline earth metal or magnesium compounds of calcium
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D2258/00—Sources of waste gases
    • B01D2258/02—Other waste gases
    • B01D2258/0283—Flue gases
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Analytical Chemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Oil, Petroleum & Natural Gas (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Treating Waste Gases (AREA)

Abstract

The invention discloses a desulfurization oxidation air system control method based on a data-driven multi-model, which comprises the following steps of S1: screening modeling data by using historical operating data of unit and CaSO3Assay concentration data is used as modeling original data; step S2: establishing an absorption column CaSO3A concentration prediction model; step S3: to real-time forecast obtained absorption tower CaSO3The concentration is calculated by adopting a sliding average method to obtain the CaSO of the absorption tower3Average concentration; step S4: controlling the start and stop of the oxidation fan; the method takes the historical data and the laboratory data stored for a long time by the unit as raw materials to carry out scheme design and modeling, does not need to carry out modeling test on the system, and has no any effect on the safe operation of the unitSounding; the soft measurement of the concentration of the calcium sulfite is realized by adopting modeling modes such as a neural network and the like, new measuring equipment is not required to be added, the frequency conversion transformation of the original oxidation air system is not required, only an industrial personal computer is required to be added, and the investment cost is low.

Description

Desulfurization oxidation air system control method based on data-driven multiple models
Technical Field
The invention belongs to the technical field of thermal power plant oxidation fan control, and particularly relates to a desulfurization oxidation air system control method based on a data-driven multi-model.
Background
The oxidizing air system is an important component of the wet desulphurization system, the power consumption of the system is high, but the oxidizing air system is prevented from being used under partial working conditionsGypsum and absorption tower slurry CaSO3The concentration exceeds the limit, a continuous operation mode is generally adopted, and the operation mode causes great energy waste, so that the on-off of the oxidizing air system according to needs has important significance for energy conservation and consumption reduction of the thermal power generating unit desulfurization system.
At present, the oxidation fan operation mode mainly has 3, is respectively: 1. the continuous operation mode of the oxidation fan; 2. slurry CaSO with absorption tower3The concentration is measured on line in real time in an oxidation fan on-off mode according to requirements; 3. the variable frequency control mode of the oxidation fan based on the fixed excess air coefficient has certain disadvantages in the prior art,
1. the continuous operation mode of the oxidation fan is as follows: most thermal power plants avoid gypsum and absorption tower slurry CaSO under partial working conditions3When the concentration exceeds the limit, the continuous operation mode of the oxidation fan is adopted, and the operation mode has large operation allowance, so that great energy waste is brought to a desulfurization system;
2. slurry CaSO with absorption tower3The method comprises the following steps of (1) starting and stopping an oxidation fan for real-time online concentration measurement according to requirements: a sulfite real-time online monitoring system is installed in a desulfurization system of an individual power plant, and sulfite real-time assay concentration is used as a judgment basis for starting and stopping a fan of an oxidation air system, but the sulfite online real-time monitoring system is expensive and large in maintenance amount, and compared with the income brought by starting and stopping the oxidation fan, the cost performance is low and is generally not accepted by power plant personnel;
3. the variable frequency control mode of the oxidation fan based on the fixed excess air coefficient is as follows: the oxidation fan that the 6kV motor that has the frequency conversion drove is adopted to some power plants, and control the frequency conversion according to fixed excess air factor, in order to reach the effect that reduces oxidation wind system power consumption, this mode also has the problem that investment cost is high earlier stage, and to most power plants, its oxidation wind system fan is roots's fan, if carry out equipment transformation, investment cost will further increase, in addition, adopt the mode of fixed excess air factor to calculate the oxidation amount of wind, for guaranteeing that oxidation wind energy can satisfy the full operating mode requirement, need select great excess air factor, can cause great energy waste equally.
Disclosure of Invention
The invention aims to provide a desulfurization oxidation air system control method based on a data-driven multi-model, which solves the problem of reducing the energy consumption of an oxidation air system by designing an intelligent start-stop scheme of an oxidation fan of a thermal power plant; the technical effects that can be produced by the preferred technical scheme in the technical schemes provided by the invention are described in detail in the following.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a desulfurization oxidation air system control method based on a data-driven multi-model, which comprises the following steps,
step S1: screening modeling data by using historical operating data of unit and CaSO3Assay concentration data is used as modeling original data;
step S2: modeling, namely taking the number of running oxidation fans including the condition that all oxidation fans are stopped as a modeling division basis, taking historical running data of a unit as an input variable of a modeling process, and taking a CaSO (catalyst absorption optical absorption System) of an absorption tower3The test concentration data is used as system output, modeling is carried out by adopting a modeling mode based on an artificial neural network driven by historical data, partial least square, support vector machine, multiple linear regression and polynomial fitting, and the CaSO of the absorption tower when different oxidation fans operate is obtained3A concentration prediction model;
step S3: to real-time forecast obtained absorption tower CaSO3The concentration is calculated by adopting a sliding average method to obtain the CaSO of the absorption tower3Average concentration;
step S4: controlling the start and the stop of the oxidation fan, namely setting the CaSO of the absorption tower when the running number n of the oxidation fans is more than or equal to 13Mean limit of concentration of
Figure BDA0002637116890000021
When the absorption tower CaSO obtained in the step S33Average concentration exceeds
Figure BDA0002637116890000022
When the oxidation fan is started, the oxidation fan is additionally started and is started at the same timeThe time k is set to 1 and then timing is carried out again until k is reached>When m is reached, recalculating the absorption tower CaSO in step S33Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower CaSO obtained in the step S33Average concentration of less than
Figure BDA0002637116890000031
Model calculation absorption tower CaSO when n oxidation fans are applied to operation3Synchronously adding a model for carrying out the operation of n-1 oxidation fans to the absorption tower CaSO while averaging the concentration3Average concentration estimation, namely applying the system input from the (k-m +1) moment to the k moment and the model when the (n-1) oxidation fans operate to carry out CaSO on the absorption tower when the k oxidation fans operate3Estimating average concentration, and estimating CaSO of the absorption tower by using two models corresponding to the working conditions of n oxidation fans and (n-1) oxidation fans3Average concentration is less than
Figure BDA0002637116890000032
Stopping an oxidation fan, setting the current time k to 1, timing again, and waiting for k>When m is reached, recalculating the absorption tower CaSO in step S33Average concentration, again according to the CaSO of the obtained absorption tower3And judging the start-stop operation of the oxidation fan by the average concentration.
Further, step S3 calculates and obtains the absorption tower CaSO3The average concentration process is that the real-time rolling prediction absorption tower CaSO is set3The time span of the concentration mean value is m, after the automatic control system of the oxidizing air is put into operation and m time passes, the CaSO of the absorption tower is started3The concentration is predicted, the current time is k, the number of running oxidation fans is n, and the CaSO of the absorption tower is at the time3Real-time prediction of concentration as rhon(k) Then there is k moment absorption tower CaSO3The average concentration was calculated as:
Figure BDA0002637116890000033
further, in step S4, when oxidizingWhen the number n of operating fans is 0, the absorption tower CaSO obtained in step S3 is used3Average concentration exceeds
Figure BDA0002637116890000034
When the time is up, an oxidation fan is started, and at the same time, the current time k is set to 1 and then the timer is restarted until the time k is up>When m is reached, recalculating the absorption tower CaSO in step S33Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower CaSO obtained in the step S33Average concentration of less than
Figure BDA0002637116890000035
The existing operating state is maintained.
Further, step S2 is implemented in the modeling process, using principal component variables obtained after principal component analysis and dimensionality reduction of unit historical operating data as input data of the modeling process, and using absorption tower CaSO3Assay concentration data is output as a system.
Further, the historical operating data of the unit in step S2 includes: oxidation wind system related variables: load and FGD (flue gas desulfurization) inlet and outlet SO of unit2Concentration, inlet and outlet O2The device comprises the following components of quantity, inlet and outlet flue gas temperature, inlet and outlet flue gas pressure, inlet and outlet flue gas humidity, original flue gas dust concentration, absorption tower PH, absorption tower slurry density, absorption tower liquid level, gypsum discharge pump flow, oxidation fan inlet and outlet temperature, oxidation fan inlet and outlet pressure and oxidation fan inlet and outlet air quantity.
Further, the historical operation data of the unit in the step S1 is directly derived from DCS, and the absorption tower CaSO3The method comprises the steps of obtaining assay concentration data from daily assay records, screening historical operation data of a unit corresponding to assay time points and assay concentrations according to assay sampling moments, and obtaining original modeling data after the assay concentrations are combined.
Further, step S1 includes screening steady state operating condition data, including,
step 1.1: selecting the data obtained in the step S1, comprehensively considering the unit capacity and the desulfurization system arrangement mode, and selecting the lowest stable stateRunning time threshold tsEach sampling instant and t before it are looked upsThe load value of the running historical data unit in the time period is greater than L when the load fluctuation in the time period is greater than LmaxDeleting the group of data to obtain steady-state modeling data;
step 1.2: applying the steady-state modeling data obtained in step 1.1 to input variable data and CaSO3Performing Pearson correlation analysis on the concentration assay data, selecting a variable with a correlation coefficient larger than 0.2 as an input variable, and performing analysis on the correlation coefficient and CaSO3The assay concentrations together make up the final modeling data.
Further, the absorption tower CaSO3Mean limit of concentration
Figure BDA0002637116890000041
The invention provides a desulfurization oxidation air system control method based on a data-driven multi-model, which has the beneficial effects that:
according to the scheme, the historical data and the laboratory data stored by the unit for a long time are used as raw materials for scheme design and modeling, a modeling test is not required to be carried out on a system, and the safe operation of the unit is not influenced; according to the scheme, soft measurement of the concentration of the calcium sulfite is realized by adopting a neural network modeling mode and the like, new measuring equipment is not required to be added, the frequency conversion transformation of an original oxidizing air system is not required, only one industrial personal computer is required to be added, and the investment cost is low; the method applies long-time-span history and test data as modeling raw materials, and the modeling result can represent the wider operation condition of the unit.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a schematic block diagram of a modeling data screening process of the present invention;
FIG. 2 is a schematic block diagram of the direct modeling of historical operational data of the unit of the present invention;
FIG. 3 is a schematic block diagram of modeling of historical operating data of the unit after principal component analysis;
FIG. 4 is a schematic block diagram of a calling mode of a unit historical operating data direct modeling model according to the present invention;
FIG. 5 is a schematic block diagram of a calling mode of a modeling model after principal component analysis of historical operating data of a unit;
FIG. 6 is a flow chart of start-stop control of the oxidation blower according to the present invention.
Detailed Description
Hereinafter, an embodiment of the data-driven multi-model-based desulfurization oxidation air system control method of the present invention will be described with reference to the accompanying drawings.
The examples described herein are specific embodiments of the present invention, are intended to be illustrative and exemplary in nature, and are not to be construed as limiting the scope of the invention. In addition to the embodiments described herein, those skilled in the art will be able to employ other technical solutions which are obvious based on the disclosure of the claims and the specification of the present application, and these technical solutions include technical solutions which make any obvious replacement or modification for the embodiments described herein.
The drawings in the present specification are schematic views to assist in explaining the concept of the present invention, and schematically show the shapes of respective portions and their mutual relationships. It is noted that the drawings are not necessarily to the same scale so as to clearly illustrate the structures of the various elements of the embodiments of the invention. Like reference numerals are used to denote like parts.
Example 1:
according to the method, when the number of operating different oxidation fans is respectively established, the related input of an oxidation air system and system absorption tower slurry CaSO3Method for modeling relationship between concentrationsFormula (II) to absorption tower CaSO3The concentration is predicated, and the on-demand start-stop of oxidation fan is carried out according to the prediction concentration, has realized that the intelligence of oxidation fan is dynamic to be stopped and has been stopped to reach the purpose that reduces oxidation wind system energy consumption by a wide margin, included:
1. screening modeling data:
(1) the historical operation and test data of the unit are used as original modeling data, the historical operation data of the oxidation air system can be directly derived from DCS, and the absorption tower CaSO3The concentration data can be obtained from daily chemical examination records, then the historical operating data of the oxidation air system corresponding to the chemical examination time point and the chemical examination concentration is screened according to the chemical examination sampling time, and the original modeling data is obtained after the chemical examination concentration is combined.
(2) In order to avoid the influence of the transition process of the system dynamic response on the modeling precision, the steady-state working condition data needs to be further screened, and the screening mode is as follows: selecting the data obtained in the step (1), comprehensively considering the unit capacity and the desulfurization system arrangement mode, and selecting the minimum stable operation time threshold tsEach sampling instant and t before it are looked upsThe load value of the running historical data unit in the time period is greater than L when the load fluctuation in the time period is greater than LmaxDeleting the group of data to obtain final modeling data; the screening process is shown in FIG. 1.
2. Modeling:
the number of the running oxidation fans including the condition that all the oxidation fans are stopped is used as a modeling division basis, and relevant variables of an oxidation air system are as follows: load and FGD (flue gas desulfurization) inlet and outlet SO of unit2Concentration, inlet and outlet O2The method comprises the steps of taking data such as quantity, inlet and outlet flue gas temperature, inlet and outlet flue gas pressure, inlet and outlet flue gas humidity, raw flue gas dust concentration, absorption tower PH, absorption tower slurry density, absorption tower liquid level, gypsum discharge pump flow, oxidation fan inlet and outlet temperature, oxidation fan inlet and outlet pressure, oxidation fan inlet and outlet air quantity and the like, or taking principal component variables obtained after the data are subjected to principal component analysis and dimensionality reduction as modeling process input data, and taking CaSO of the absorption tower as principal component input data3And (3) taking the assay concentration data as system output, and adopting a historical data-driven modeling mode, such as: by artificial meansModeling by data driving modeling modes such as neural network, partial least square, support vector machine, multiple linear regression, polynomial fitting and the like to obtain CaSO of the absorption tower when different oxidation fans operate3A concentration model; the modeling process is shown in fig. 2 and 3.
3. Controlling the start and the stop of the oxidation fan:
loading of unit, FGD inlet and outlet SO2Concentration, inlet and outlet O2The method comprises the steps of taking data such as quantity, inlet and outlet flue gas temperature, inlet and outlet flue gas pressure, inlet and outlet flue gas humidity, raw flue gas dust concentration, pH of an absorption tower, slurry density of the absorption tower, liquid level of the absorption tower, gypsum discharge pump flow, oxidation fan inlet and outlet temperature, oxidation fan inlet and outlet pressure, oxidation fan inlet and outlet air quantity and the like, or principal component variables formed by weighting and combining the data as input data, and predicting CaSO of the absorption tower by applying a model built in step 23The model calling method for the concentrations is shown in fig. 4 and 5.
And taking the predicted concentration as a judgment basis for increasing or decreasing the number of the running oxidation fans, wherein the judgment process is as follows:
(1) calculating the CaSO of the absorption tower3Average concentration:
to real-time forecast obtained absorption tower CaSO3The concentration adopts a sliding average mode to obtain CaSO of the absorption tower3Average concentration, real-time rolling prediction of absorption tower CaSO3The time span of the concentration mean value is m, after the automatic control system of the oxidizing air is put into operation and m time passes, the CaSO of the absorption tower is started3The concentration is predicted, the current time is k, the number of running oxidation fans is n, and the CaSO of the absorption tower is at the time3Real-time prediction of concentration as rhon(k) Then there is k moment absorption tower CaSO3The average concentration was calculated as:
Figure BDA0002637116890000071
(2) oxidation fan operation number control
1) When the number n of the operating oxidation fans is more than or equal to 1, the CaSO of the absorption tower3Mean limit of concentration of
Figure BDA0002637116890000072
General selection
Figure BDA0002637116890000073
When the absorption tower obtained in 1) is CaSO3Average concentration exceeds
Figure BDA0002637116890000074
When the time is up, an oxidation fan is started, and at the same time, the current time k is set to 1 and then the timer is restarted until the time k is up>When m is reached, recalculating the absorption tower CaSO according to 1)3Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower obtained in 1) is CaSO3Average concentration of less than
Figure BDA0002637116890000075
Model calculation absorption tower CaSO when n oxidation fans are applied to operation3Synchronously adding a model for carrying out the operation of n-1 oxidation fans to the absorption tower CaSO while averaging the concentration3Average concentration estimation, namely applying the system input from the (k-m +1) moment to the k moment and the model when the (n-1) oxidation fans operate to carry out CaSO on the absorption tower when the k oxidation fans operate3Estimating average concentration, and estimating CaSO of the absorption tower by using two models corresponding to the working conditions of n oxidation fans and (n-1) oxidation fans3Average concentration is less than
Figure BDA0002637116890000081
Stopping an oxidation fan, setting the current time k to 1, timing again, and waiting for k>When m is reached, recalculating the absorption tower CaSO according to 1)3Average concentration, again according to the CaSO of the obtained absorption tower3And judging the start-stop operation of the oxidation fan by the average concentration.
2) When the number n of the oxidation fans is equal to 0, the absorption tower CaSO obtained in the step 1) is used3Average concentration exceeds
Figure BDA0002637116890000082
When, increaseStarting an oxidation fan, setting the current time k to 1, timing again, and waiting for k>When m is reached, recalculating the absorption tower CaSO according to 1)3Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower obtained in 1) is CaSO3Average concentration of less than
Figure BDA0002637116890000083
Keeping the existing running state; the flow chart of the start-stop control of the oxidation fan is shown in fig. 6.
Example 2:
the method for controlling the oxidizing air system based on the data model and the mechanism operation can be further improved as follows, when the steady-state working condition is further screened from the modeling data, the stable running time of the load working condition before and after the sampling time is not taken as a steady-state judgment basis, and the stable running time of variables such as the flue gas flow, the FGD inlet sulfur content or the flue gas flow multiplied by the FGD inlet sulfur content before and after the sampling time is taken as a steady-state judgment basis; the method establishes the relevant variables of the absorption tower and CaSO in the slurry of the absorption tower3Model between concentrations, due to elimination of assay absorption tower CaSO in the actual assay process3In addition to the concentration, the CaSO in the dehydrated gypsum can be treated3The concentration is tested, so that the related variables of the absorption tower and CaSO in the dehydrated gypsum can be established3And (4) carrying out approximate replacement on the original model by using the model between concentrations.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (8)

1. A desulfurization oxidation air system control method based on data-driven multiple models is characterized by comprising the following steps,
step S1: screening modeling data by adopting unit historical operationData and CaSO3Assay concentration data is used as modeling original data;
step S2: modeling, namely taking the number of running oxidation fans including the condition that all oxidation fans are stopped as a modeling division basis, taking historical running data of a unit as an input variable of a modeling process, and taking a CaSO (catalyst absorption optical absorption System) of an absorption tower3The test concentration data is used as system output, modeling is carried out by adopting a modeling mode based on an artificial neural network driven by historical data, partial least square, support vector machine, multiple linear regression and polynomial fitting, and the CaSO of the absorption tower when different oxidation fans operate is obtained3A concentration prediction model;
step S3: to real-time forecast obtained absorption tower CaSO3The concentration is calculated by adopting a sliding average method to obtain the CaSO of the absorption tower3Average concentration;
step S4: controlling the start and the stop of the oxidation fan, namely setting the CaSO of the absorption tower when the running number n of the oxidation fans is more than or equal to 13Mean limit of concentration of
Figure FDA0002637116880000011
When the absorption tower CaSO obtained in the step S33Average concentration exceeds
Figure FDA0002637116880000012
When the time is up, an oxidation fan is started, and at the same time, the current time k is set to 1 and then the timer is restarted until the time k is up>When m is reached, recalculating the absorption tower CaSO in step S33Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower CaSO obtained in the step S33Average concentration of less than
Figure FDA0002637116880000013
Model calculation absorption tower CaSO when n oxidation fans are applied to operation3Synchronously adding a model for carrying out the operation of n-1 oxidation fans to the absorption tower CaSO while averaging the concentration3Average concentration estimation, namely applying system input from time (k-m +1) to time k and model input when (n-1) oxidation fans operateCaSO (absorbed SO) of oxidation fan time during k time3Estimating average concentration, and estimating CaSO of the absorption tower by using two models corresponding to the working conditions of n oxidation fans and (n-1) oxidation fans3Average concentration is less than
Figure FDA0002637116880000014
Stopping an oxidation fan, setting the current time k to 1, timing again, and waiting for k>When m is reached, recalculating the absorption tower CaSO in step S33Average concentration, again according to the CaSO of the obtained absorption tower3And judging the start-stop operation of the oxidation fan by the average concentration.
2. The method for controlling the desulfurization oxidation air system based on the data-driven multi-model as claimed in claim 1, wherein the step S3 is implemented by calculating and obtaining CaSO of the absorption tower3The average concentration process is that the real-time rolling prediction absorption tower CaSO is set3The time span of the concentration mean value is m, after the automatic control system of the oxidizing air is put into operation and m time passes, the CaSO of the absorption tower is started3The concentration is predicted, the current time is k, the number of running oxidation fans is n, and the CaSO of the absorption tower is at the time3Real-time prediction of concentration as rhon(k) Then there is k moment absorption tower CaSO3The average concentration was calculated as:
Figure FDA0002637116880000021
3. the method as claimed in claim 1, wherein in step S4, when the number n of the oxidation fans is 0, the absorption tower CaSO obtained in step S3 is used as the absorption tower CaSO3Average concentration exceeds
Figure FDA0002637116880000022
When the time is up, an oxidation fan is started, and at the same time, the current time k is set to 1 and then the timer is restarted until the time k is up>m, according to step S3Calculating the CaSO of the absorption tower3Average concentration, again according to the CaSO of the obtained absorption tower3Judging the start-stop operation of the oxidation fan according to the average concentration; when the absorption tower CaSO obtained in the step S33Average concentration of less than
Figure FDA0002637116880000023
The existing operating state is maintained.
4. The method for controlling the desulfurization and oxidation air system based on the data-driven multiple models as claimed in claim 1, wherein step S2 is implemented in a modeling process, a principal component variable obtained after principal component analysis and dimensionality reduction are implemented on historical unit operation data is used as input data of the modeling process, and an absorption tower CaSO is used as input data of the modeling process3Assay concentration data is output as a system.
5. The method for controlling a desulfurization oxidation air system based on data-driven multiple models according to claim 1, wherein the historical operating data of the unit in the step S2 includes: oxidation wind system related variables: load and FGD (flue gas desulfurization) inlet and outlet SO of unit2Concentration, inlet and outlet O2The device comprises the following components of quantity, inlet and outlet flue gas temperature, inlet and outlet flue gas pressure, inlet and outlet flue gas humidity, original flue gas dust concentration, absorption tower PH, absorption tower slurry density, absorption tower liquid level, gypsum discharge pump flow, oxidation fan inlet and outlet temperature, oxidation fan inlet and outlet pressure and oxidation fan inlet and outlet air quantity.
6. The method for controlling a desulfurization oxidation air system based on data-driven multiple models of claim 1, wherein the historical operating data of the unit in step S1 is directly derived from DCS, and the absorption tower CaSO3The method comprises the steps of obtaining assay concentration data from daily assay records, screening historical operation data of a unit corresponding to assay time points and assay concentrations according to assay sampling moments, and obtaining original modeling data after the assay concentrations are combined.
7. The method for controlling a desulfurization oxidizing air system based on data-driven multiple models according to claim 1, wherein the step S1 further comprises screening steady-state condition data including,
step 1.1: selecting the data acquired in the step S1, comprehensively considering the unit capacity and the desulfurization system arrangement mode, and selecting the minimum stable operation time threshold tsEach sampling instant and t before it are looked upsThe load value of the running historical data unit in the time period is greater than L when the load fluctuation in the time period is greater than LmaxDeleting the group of data to obtain steady-state modeling data;
step 1.2: applying the steady-state modeling data obtained in step 1.1 to input variable data and CaSO3Performing Pearson correlation analysis on the concentration assay data, selecting a variable with a correlation coefficient larger than 0.2 as an input variable, and performing analysis on the correlation coefficient and CaSO3The assay concentrations together make up the final modeling data.
8. The data-driven multi-model-based desulfurization and oxidation air system control method according to claim 1, wherein the absorption tower CaSO3Mean limit of concentration
Figure FDA0002637116880000031
CN202010828597.4A 2020-08-18 2020-08-18 Desulfurization oxidation air system control method based on data-driven multiple models Active CN112044270B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010828597.4A CN112044270B (en) 2020-08-18 2020-08-18 Desulfurization oxidation air system control method based on data-driven multiple models

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010828597.4A CN112044270B (en) 2020-08-18 2020-08-18 Desulfurization oxidation air system control method based on data-driven multiple models

Publications (2)

Publication Number Publication Date
CN112044270A true CN112044270A (en) 2020-12-08
CN112044270B CN112044270B (en) 2022-09-02

Family

ID=73600697

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010828597.4A Active CN112044270B (en) 2020-08-18 2020-08-18 Desulfurization oxidation air system control method based on data-driven multiple models

Country Status (1)

Country Link
CN (1) CN112044270B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113082954A (en) * 2021-04-07 2021-07-09 浙江大学 Whole-process intelligent operation regulation and control system of wet desulphurization device

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013057442A (en) * 2011-09-08 2013-03-28 Chugoku Electric Power Co Inc:The Device and method for providing operation information of gas mixing fan
CN105138718A (en) * 2015-07-10 2015-12-09 广东电网有限责任公司电力科学研究院 Novel calculation method for desulphurization efficiency of desulphurization tower in thermal power plant in combination with UDF and auxiliary machine load adjustment method adopting calculation method
CN105467954A (en) * 2015-11-20 2016-04-06 华北电力大学（保定） Limestone-gypsum-method-based desulphurization ORP and pH dual-control method and apparatus
CN108317100A (en) * 2018-04-04 2018-07-24 华润电力（沧州运东）有限公司 A kind of air compressor control method, equipment and system
CN208229629U (en) * 2018-04-10 2018-12-14 安徽科力德电力科技有限公司 A kind of wet flue gas desulfurization oxidation energy-saving control device of blower
CN109085856A (en) * 2018-09-10 2018-12-25 厦门邑通软件科技有限公司 A kind of desulfuration absorbing tower oxidation fan consumption-reducing method and system
CN109636115A (en) * 2018-11-13 2019-04-16 北京国电龙源环保工程有限公司 Absorption tower slurries calcium sulfite concentration approximating method and system based on big data

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013057442A (en) * 2011-09-08 2013-03-28 Chugoku Electric Power Co Inc:The Device and method for providing operation information of gas mixing fan
CN105138718A (en) * 2015-07-10 2015-12-09 广东电网有限责任公司电力科学研究院 Novel calculation method for desulphurization efficiency of desulphurization tower in thermal power plant in combination with UDF and auxiliary machine load adjustment method adopting calculation method
CN105467954A (en) * 2015-11-20 2016-04-06 华北电力大学（保定） Limestone-gypsum-method-based desulphurization ORP and pH dual-control method and apparatus
CN108317100A (en) * 2018-04-04 2018-07-24 华润电力（沧州运东）有限公司 A kind of air compressor control method, equipment and system
CN208229629U (en) * 2018-04-10 2018-12-14 安徽科力德电力科技有限公司 A kind of wet flue gas desulfurization oxidation energy-saving control device of blower
CN109085856A (en) * 2018-09-10 2018-12-25 厦门邑通软件科技有限公司 A kind of desulfuration absorbing tower oxidation fan consumption-reducing method and system
CN109636115A (en) * 2018-11-13 2019-04-16 北京国电龙源环保工程有限公司 Absorption tower slurries calcium sulfite concentration approximating method and system based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113082954A (en) * 2021-04-07 2021-07-09 浙江大学 Whole-process intelligent operation regulation and control system of wet desulphurization device
CN113082954B (en) * 2021-04-07 2022-04-01 浙江大学 Whole-process intelligent operation regulation and control system of wet desulphurization device

Also Published As

Publication number Publication date
CN112044270B (en) 2022-09-02

Similar Documents

Publication Publication Date Title
CN112506162B (en) Oxidation air system control method based on data model and mechanism operation
CN111340199A (en) Desulfurization system energy-saving method based on material balance and deep learning
CN101477367A (en) Energy-saving consumption reduction oriented process efficiency optimization control method and use thereof
CN111897373B (en) Model prediction-based ammonia injection flow adjusting method for SCR denitration device
CN112465239B (en) Desulfurization system operation optimization method based on improved PSO-FCM algorithm
CN101303595A (en) Open ring fuzzy control system for wet process FGD system
CN105498497A (en) Flue gas desulfurization and denitration integrated equipment controlled through multiple variables and control method thereof
CN112044270B (en) Desulfurization oxidation air system control method based on data-driven multiple models
CN110992209A (en) Flow prediction method
CN105697166A (en) Systems and Methods for Controlling Air-to-Fuel Ratio Based on Catalytic Converter Performance
CN111598308B (en) Method for solving combination optimization of slurry circulating pump based on regression and double PSO algorithm
CN113112060A (en) Lifting pump station scheduling method and device based on soft flow measurement and computer equipment
CN113530793A (en) Intelligent adjusting system for air compression station
US7155367B1 (en) Method for evaluating relative efficiency of equipment
CN113505497B (en) Method and system for monitoring slurry quality of wet flue gas desulfurization absorption tower
CN111008735B (en) Predictive emissions management system and method
KR20060092660A (en) Automatic control device and method for wastewater treatment using fuzzy control
Chan et al. Economic model predictive control of an absorber-stripper CO2 capture process for improving energy cost
CN112415148B (en) Wet flue gas desulfurization system CaSO based on online learning 3 Soft measurement method
CN113468813B (en) Desulfurization system inlet SO2Concentration prediction method and device and electronic equipment
Zhang et al. Development of a new control method for the dynamic liquid desiccant dehumidification process
CN114444391A (en) Oxidation fan operation optimization algorithm based on particle swarm optimization
CN109340094A (en) A kind of air compressor energy-saving control method based on load
CN114880944A (en) Desulfurization system operation optimization method and system based on Lasso algorithm
CN115869745B (en) Desulfurization treatment method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information
CB02 Change of applicant information

Address after: 100039 room 901, 9 / F, building 1, yard 16, West Fourth Ring Middle Road, Haidian District, Beijing

Applicant after: Guoneng Longyuan environmental protection Co.,Ltd.

Address before: 100039 room 911, 1 building, 16 West Fourth Ring Road, Haidian District, Beijing.

Applicant before: BEIJING GUODIAN LONGYUAN ENVIRONMENTAL ENGINEERING Co.,Ltd.

GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20221025

Address after: 100039 room 901, 9 / F, building 1, yard 16, West Fourth Ring Middle Road, Haidian District, Beijing

Patentee after: Guoneng Longyuan environmental protection Co.,Ltd.

Patentee after: Guoneng (Shandong) energy environment Co.,Ltd.

Address before: 100039 room 901, 9 / F, building 1, yard 16, West Fourth Ring Middle Road, Haidian District, Beijing

Patentee before: Guoneng Longyuan environmental protection Co.,Ltd.