CN115096106A - Multi-target progressive optimization and early warning method for heating furnace - Google Patents
Multi-target progressive optimization and early warning method for heating furnace Download PDFInfo
- Publication number
- CN115096106A CN115096106A CN202210779367.2A CN202210779367A CN115096106A CN 115096106 A CN115096106 A CN 115096106A CN 202210779367 A CN202210779367 A CN 202210779367A CN 115096106 A CN115096106 A CN 115096106A
- Authority
- CN
- China
- Prior art keywords
- temperature
- furnace
- curve
- blank
- target
- 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
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 74
- 238000010438 heat treatment Methods 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000000750 progressive effect Effects 0.000 title claims abstract description 34
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 31
- 231100000719 pollutant Toxicity 0.000 claims abstract description 31
- 230000008878 coupling Effects 0.000 claims abstract description 29
- 238000010168 coupling process Methods 0.000 claims abstract description 29
- 238000005859 coupling reaction Methods 0.000 claims abstract description 29
- 230000007246 mechanism Effects 0.000 claims abstract description 28
- 230000001360 synchronised effect Effects 0.000 claims abstract description 21
- 238000005265 energy consumption Methods 0.000 claims abstract description 12
- 238000002485 combustion reaction Methods 0.000 claims abstract description 8
- 238000005259 measurement Methods 0.000 claims description 30
- 239000000446 fuel Substances 0.000 claims description 25
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 13
- 230000001105 regulatory effect Effects 0.000 claims description 13
- 230000001276 controlling effect Effects 0.000 claims description 8
- 239000003345 natural gas Substances 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
- 239000001301 oxygen Substances 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 4
- 108010074864 Factor XI Proteins 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 210000001161 mammalian embryo Anatomy 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 239000000356 contaminant Substances 0.000 claims description 2
- 238000011109 contamination Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 238000005070 sampling Methods 0.000 abstract 1
- 230000000087 stabilizing effect Effects 0.000 abstract 1
- 230000006870 function Effects 0.000 description 18
- 229910000831 Steel Inorganic materials 0.000 description 13
- 239000010959 steel Substances 0.000 description 13
- 230000033764 rhythmic process Effects 0.000 description 6
- 238000005096 rolling process Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000010079 rubber tapping Methods 0.000 description 4
- 238000002791 soaking Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 238000013021 overheating Methods 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 241000287828 Gallus gallus Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/04—Arrangements of indicators or alarms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Primary Health Care (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Control Of Heat Treatment Processes (AREA)
Abstract
The invention provides a multi-target progressive optimization and early warning method for a heating furnace, which relates to the field of automation of metallurgical equipment and comprises the following steps of establishing a furnace temperature and blank temperature synchronous coupling mechanism model under the same working condition; acquiring current furnace condition parameters; the current furnace condition parameters are brought into a furnace temperature and blank temperature synchronous coupling mechanism model, and the unit time internal combustion consumption, pollutants and the deviation of the burning loss and the target value are brought into a total optimization function to obtain a total optimization objective function; according to the method, based on a mechanism-intelligent cooperative blank temperature and furnace temperature coupling model, the index requirement of accurately controlling the temperature and the consumption of each section is carried out according to indexes such as temperature control precision, energy consumption, pollutant emission, burning loss and the like, and multi-target progressive intelligent setting is implemented; on the basis, the method carries out sample sorting and sampling of faults or accidents, implements trend prejudgment, accident early warning and timely treatment, and achieves the purposes of stabilizing the furnace condition, improving the temperature control precision and obviously improving the environmental protection and energy saving indexes.
Description
Technical Field
The invention relates to the field of automation of metallurgical equipment, in particular to a multi-target progressive optimization and early warning method for a heating furnace.
Background
With the increasing requirements of rolled pieces on the organization performance and the shape and size precision of rolled pieces by the rolling line, the related key technology starts to track and trace to obtain the optimal rolling condition on the whole. Such as heavy iron scale, edge cracking, large deformation resistance, high energy consumption and the like in the rolling process, are related to the blank heating path, the tapping temperature precision and the internal and external uniformity of the heating furnace to a great extent. For the heating furnace, besides the defects of under-burning, over-burning, overheating, high temperature fluctuation, large internal and external temperature difference and the like of the blank are avoided, the energy consumption index of the heating furnace and the pollution index of the smoke emission need to be considered. Thus, the steel-burning process of the heating furnace is a typical multi-objective optimization problem.
The existing heating furnace optimization model mostly adopts the total evaluation functions of furnace outlet temperature, internal and external temperature difference, even energy consumption and burning loss and respective weighting. This can reflect the basic efficiency and accuracy of steel burning to some extent.
However, the existing overall evaluation function has the following problems: firstly, the forecasting precision of the blank temperature and the furnace temperature is not accurate, wherein the furnace temperature adopts a furnace top or furnace wall thermocouple temperature measuring value, the temperature change near the blank cannot be reflected, the blank temperature cannot be measured in real time, and the inner and outer temperature curves of the blank cannot be accurately forecasted due to the model forecasting precision; and secondly, an early warning mechanism is lacked, trend prediction and fault early warning cannot be given to the problems of steel burning rhythm change, furnace pressure fluctuation, unstable air-fuel ratio, residual oxygen content, pollutant standard exceeding and the like, dynamic boundary conditions are rarely considered in a mathematical model, so that time lag and disturbance of furnace temperature and blank temperature are obvious, and a high-precision mechanism prediction model and big data intelligent analysis are lacked. In conclusion, a heating furnace optimization method with an early warning mechanism and capable of accurately forecasting the blank temperature and the furnace temperature is to be invented.
Disclosure of Invention
The invention provides a multi-target progressive optimization and early warning method for a heating furnace, which solves the problem that the total evaluation function of the existing heating furnace optimization model cannot accurately forecast the temperature of an embryo and the temperature of the furnace. The steel burning system and the measurement and control system of the existing heating furnace are technically improved, so that the steel burning efficiency and the heating furnace capacity are improved, the production rhythm of a rolling mill is better matched, and the requirements of the organization performance, the shape and the size indexes of a final product are met.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a heating furnace multi-target progressive optimization method based on mechanism-intelligent cooperation comprises the following steps:
establishing a furnace temperature and blank temperature synchronous coupling mechanism model under the same working condition;
acquiring a blank temperature soft measurement forecasting curve and an oven temperature soft measurement forecasting curve based on the oven temperature and blank temperature synchronous coupling mechanism model;
acquiring a furnace temperature standard deviation curve and a blank temperature standard deviation curve based on the blank temperature soft measurement forecast curve and the furnace temperature soft measurement forecast curve;
taking the furnace temperature standard deviation curve and the blank temperature standard deviation curve as convergence conditions, firstly performing coupled calculation of the furnace temperature blank temperature, and synchronously iterating to obtain a forecast curve of the optimal blank temperature;
setting the outlet blank temperature and the internal and external temperature difference as first optimization conditions in the optimal blank temperature furnace temperature prediction curve to ensure that the outlet blank temperature always meets a target set value;
setting different weighting factors according to target requirements, setting the deviation and the weighting factor of the fuel consumption and the target value in unit time, setting the deviation and the weighting factor of pollutants and the target value, and setting the deviation and the weighting factor of the burning loss and the target value;
substituting the deviation and the weight factor of the fuel consumption and the target value in unit time, the deviation and the weight factor of the pollutant and the target value, and the deviation and the weight factor of the set burning loss and the target value into a synchronous coupling mechanism model of the temperature of the blank temperature furnace to obtain a multi-objective optimization function;
carrying out progressive iteration solution on the multi-target optimization function;
regulating and controlling the air-fuel ratio in the furnace to make the furnace temperature curve and the blank temperature curve respectively consistent with the furnace temperature target curve and the blank temperature target curve under the current working condition;
on the basis of meeting the temperature control precision of the furnace temperature and the blank temperature, optimizing and controlling the natural gas consumption in unit time to be minimum, and simultaneously ensuring that the temperature curves of the furnace temperature and the blank temperature are consistent with the blank temperature target curve under the current working condition and the furnace temperature target curve under the current working condition;
on the basis of meeting the minimum requirements of furnace temperature, blank temperature control precision and natural gas fuel consumption, the pollutants are optimally regulated and controlled to be minimum, so that the pollutant discharge amount reaches the specified standard;
on the basis of meeting the requirements of furnace temperature, billet temperature control precision, minimum natural gas burnup and minimum regulation and control pollutants, the heating time of the billet is optimally regulated and controlled, so that the burning loss of the billet reaches the specified standard.
Further, the furnace temperature and blank temperature synchronous coupling mechanism model is established by the following steps:
coupling environment of the virtual heating furnace and the blank;
the hearth is regarded as a physical body to carry out dynamic field quantity modeling;
setting the temperature of the hearth and the temperature of the blank as boundary conditions;
and obtaining a furnace temperature and blank temperature synchronous coupling mechanism model.
Further, the error characteristic values of the current furnace conditions include furnace pressure fluctuation, air/gas flow and pressure fluctuation, and residual oxygen amount.
Further, the soft measurement forecast curve of the furnace temperature has the formula:
T f ＝A+Bx+Cx 2 +Dx 3 +Ex 4 +Fx 5
the formula of the soft measurement forecast curve of the blank temperature is as follows:
T s ＝a+bx+cx 2 +dx 3 +ex 4 +fx 5
the formula of the furnace temperature standard curve is as follows:
the formula of the blank temperature standard curve is as follows:
wherein x is a heating furnace length coordinate, A, B, C, D, E, F is a furnace temperature target characteristic coefficient, and a, b, c, d, e and f are blank temperature target characteristic coefficients of each order;
the formula of the furnace temperature target curve of the current working condition is as follows:
the formula of the blank temperature target curve under the current working condition is as follows:
wherein,
is the furnace temperature error characteristic value of the current furnace condition,
the characteristic value of the blank temperature error of the current furnace condition is obtained;
the formula of the furnace temperature regulation deviation curve is as follows:
the formula of the embryo temperature regulation deviation curve is as follows:
ΔT s ＝T s -T s +
wherein, T f Is the actual furnace temperature, T s The actual blank temperature is obtained;
setting n nodes divided along the furnace length, wherein the formula of the furnace temperature standard deviation curve is as follows:
wherein, Delta T fi Is the ith point temperature value mu on the current furnace temperature curve f Is the average furnace temperature;
the formula of the blank temperature standard deviation curve is as follows:
wherein, Delta T si Is the ith point temperature value, mu, on the current furnace temperature curve s Is the average furnace temperature;
setting a weight factor xi of the furnace temperature and the blank temperature, wherein a formula of a forecast curve of the optimal blank temperature furnace temperature is as follows:
ψ T ＝min[ξσ f +(1-ξ)σ s ]
the optimization function of the outlet blank temperature and the temperature difference between the inside and the outside is as follows:
wherein, T sout Is the surface temperature of the furnace blank,
as a blank surface target value, Δ T sout Is the temperature difference between the inside and the outside of the outlet blank,
zeta outlet blank temperature weight factor for target deviation;
the multi-objective optimization function is:
ψ＝w T ψ T +w Tout ψ Tout +w 1 ΔG 1 +w 2 ΔG 2 +w 3 ΔG 3
wherein, Δ G 1 Deviation of burnup per unit time from a target value, Δ G 2 Δ G deviation of the contamination from the target value 3 Deviation of burn-out from target value, w 1 Is a weight factor of burnup per unit time, w 2 Is a weight factor of the contaminant, w 3 Weight factor for burn-up, w T Weight factor, w, of a blank furnace temperature coupling optimization function Tout And the weight factor of the outlet billet temperature optimization function.
Further, in the step of performing progressive iterative solution on the multi-objective optimization function, the furnace temperature and the blank temperature are first targets, the productivity is a second target, the unit time fuel consumption is a third target, the pollutant is a fourth target, the burning loss is a fifth target, and the weight factors of the first target, the second target, the third target, the fourth target and the fifth target are sequentially decreased; or adjusting the sequence or weight factor proportion of the optimization target according to the actual requirements of the site.
Further, in the step of performing progressive iterative solution on the multi-objective optimization function, iterative optimization is performed on a first objective, and iterative optimization is performed on a second objective under the condition that the first objective is met; if the first target is out of limit in the iteration process, the first target iteration optimization is carried out again; and if the first target does not exceed the limit, performing third target iterative optimization until the fifth target iterative optimization is completed.
A heating furnace multi-target progressive early warning method based on mechanism-intelligent cooperation is realized based on any one heating furnace multi-target progressive optimization method based on mechanism-intelligent cooperation, and comprises the following steps:
collecting working condition signals of the heating furnace in real time;
bringing the working condition signal of the heating furnace into the furnace temperature and blank temperature synchronous coupling mechanism model to obtain a temperature prediction curve of the whole furnace condition;
carrying out black box test verification on the temperature prediction curve of the whole furnace condition, optimizing the actual boundary condition of the model, improving the prediction precision, carrying out high-precision soft measurement on the blank temperature and the furnace temperature, and obtaining a standard temperature rise curve set;
centrally selecting a curve with the minimum temperature difference between the discharged blank and the inside and the outside as a standard temperature rise curve from the standard temperature rise curve, and superposing the standard temperature rise curve on the error characteristic value of the current working condition to obtain a target temperature rise curve;
subtracting the actual curves of the blank temperature and the furnace temperature from the target temperature rise curve to obtain a temperature deviation curve;
judging whether the predicted temperature-rise curve is matched with a target temperature-rise curve or not according to the mean square deviation of the temperature deviation curve, if not, utilizing a Level1 regulation system to carry out real-time regulation and control, combining an air-fuel ratio double-crossing amplitude limiting rule, and implementing self-adaptive quantitative control of the furnace temperature, so that the blank temperature and the furnace temperature quickly reach a stable state and meet a set temperature target; when the target temperature rise curves are matched, multi-target weighted optimization is carried out on the target temperature rise curves, productivity, energy consumption, burning loss and pollutant emission indexes are considered, a Level2 combustion control system is used for carrying out self-learning optimization regulation and control, and a more optimal target temperature rise curve is made.
Further, judging whether the predicted temperature rise curve of the actual measurement or the model soft measurement of the black box is consistent with the target temperature rise curve or not, comprising the following steps:
classifying and setting thresholds of all accidents or faults of the heating furnace, taking a good stable state and a weighted set value of a theoretical value as a standard threshold, taking an unstable accident state as a standard sample of an early warning range, and judging the type and degree of the accident after data comparison is carried out on current working condition data and the standard threshold;
when a slight accident happens, a Level1 regulation and control system or a Level2 combustion control system is adopted for automatic processing;
when the accident is moderate, reminding people to participate in maintenance operation;
when a serious accident occurs, sound and light alarm is carried out in advance, and an emergency plan is started;
the standard threshold is as follows:
wherein:
in order to pre-alarm the target for the temperature,
in order to pre-alarm the upper limit,
the lower limit of the early warning is set.
The invention has the beneficial effects that:
the method is based on the furnace temperature/blank temperature curve, the energy consumption curve and the pollutant emission index of the whole furnace condition, establishes a furnace temperature and blank temperature synchronous coupling mechanism model, and carries out multi-target progressive optimization based on the model, improves the forecasting precision of the model, not only facilitates the physical mechanism analysis combined with the mechanism model, accurately calculates the furnace atmosphere according to the characteristics of different materials, specifications and the like, and obtains the blank temperature, the furnace temperature, the energy consumption, the pollutant and other indexes which accord with the actual rule, thereby making the blank temperature and the furnace temperature heating curve of different weight factors according to the furnace condition steel burning rhythm, the steel tapping yield, the energy consumption and the environmental protection indexes, and designing the current furnace condition characteristic in a targeted manner to realize accurate steel burning;
the invention provides a heating furnace multi-target progressive early warning method based on mechanism-intelligent cooperation, which is convenient for carrying out big data fault prejudgment and analysis on a heating furnace, thereby being capable of carrying out early warning and pretreatment on problems such as overheating, overburning, under burning, burnup, black smoke, edge cracking, burning loss and the like, eliminating potential hidden dangers and avoiding accidents. In summary, the invention aims to realize stable steel burning and accident early warning of the heating furnace and improve the combustion efficiency and production capacity of the heating furnace to the maximum extent by providing a heating furnace multi-target progressive early warning method based on mechanism-intelligent cooperation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings needed to be 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.
FIG. 1 shows a multi-objective progressive regulation strategy for synchronously coupling the blank temperature and the furnace temperature of the heating furnace.
FIG. 2 is a technical route of the heating furnace for multi-target intelligent setting and accident early warning.
FIG. 3 is a schematic diagram of the priority mechanism for the synchronous control of the whole furnace condition.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus that are known by one of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The invention provides a technical scheme that: a heating furnace multi-target progressive optimization method based on mechanism-intelligent cooperation comprises the following steps:
s1, establishing a furnace temperature and blank temperature synchronous coupling mechanism model under the same working condition;
s2, obtaining a soft measurement forecast curve of the blank temperature and a soft measurement forecast curve of the furnace temperature based on the furnace temperature and blank temperature synchronous coupling mechanism model;
s3, acquiring a blank temperature target curve under the current working condition and a furnace temperature target curve under the current working condition;
s311, acquiring data of the furnace temperature and the blank temperature actually measured on site;
s312, bringing the data of the furnace temperature and the blank temperature actually measured on site into a temperature and blank temperature synchronous coupling mechanism model to obtain a blank temperature standard curve set and a furnace temperature standard curve set;
s313, selecting a curve with the blank temperature standard curve concentrated tapping blank temperature and the minimum inner and outer temperature difference as a blank temperature standard curve, and selecting a curve with the furnace temperature standard curve concentrated tapping furnace temperature and the minimum inner and outer temperature difference as a furnace temperature standard curve;
s314, superposing the blank temperature standard curve and the furnace temperature standard curve with the error characteristic value of the current furnace condition to obtain a blank temperature target curve of the current working condition and a furnace temperature target curve of the current working condition;
s4, subtracting the blank temperature soft measurement forecast curve from the blank temperature target curve under the current working condition to obtain an actual blank temperature regulation deviation curve, and comparing the furnace temperature soft measurement forecast curve with the furnace temperature target curve under the current working condition to obtain an actual furnace temperature regulation deviation curve;
s5, performing mean square error processing on the actual furnace temperature regulation deviation curve to obtain a furnace temperature standard deviation curve, and performing mean square error processing on the actual blank temperature regulation deviation curve to obtain a blank temperature standard deviation curve;
s6, taking the furnace temperature standard deviation curve and the blank temperature standard deviation curve as convergence conditions, firstly performing coupled calculation of the furnace temperature blank temperature, and synchronously iterating to obtain a forecasting curve of the optimal blank temperature;
s7, setting the outlet blank temperature and the internal and external temperature difference as first optimization conditions in the optimal blank temperature furnace temperature prediction curve, so that the outlet blank temperature always meets a target set value;
s8, setting different weighting factors according to target requirements, setting deviation and weighting factors of the fuel consumption and the target value in unit time, setting deviation and weighting factors of pollutants and the target value, and setting deviation and weighting factors of the burning loss and the target value;
s9, substituting the deviation and the weight factor of the fuel consumption and the target value in unit time, the deviation and the weight factor of the pollutant and the target value, and the deviation and the weight factor of the set burning loss and the target value into a blank temperature furnace temperature synchronous coupling mechanism model to obtain a multi-objective optimization function;
s10, carrying out progressive iterative solution on the multi-objective optimization function;
s101, regulating and controlling the air-fuel ratio in the furnace to enable the furnace temperature and blank temperature curves to be respectively consistent with the furnace temperature target curve of the current working condition and the blank temperature target curve of the current working condition of S3;
s102, optimizing and regulating the natural gas consumption to be minimum in unit time on the basis of meeting the temperature control precision of S101, and simultaneously ensuring that the temperature curves of the furnace temperature and the blank temperature are consistent with the target curve of S3;
s103, optimizing and regulating pollutants to be minimum on the basis of meeting the requirements of S101-S102, so that the pollutant discharge amount reaches the specified standard;
and S104, optimizing and regulating the heating time of the steel billet on the basis of meeting the requirements of S101-S103, so that the burning loss of the steel billet reaches the specified standard.
As shown in fig. 1, firstly, according to the current furnace condition parameters, the synchronous coupling model of the blank temperature and the furnace temperature is utilized to calculate the transient blank temperature and furnace temperature distribution condition of the current state in real time, grasp the actual operation state of the whole heating furnace, and obtain the soft measurement prediction curve of the blank temperature and the furnace temperature. Meanwhile, according to the error characteristic values (furnace pressure fluctuation, air/gas flow, pressure fluctuation, residual oxygen amount and the like) of the current furnace conditions, the error characteristic values are respectively superposed with the blank temperature and furnace temperature standard curves selected preferentially to form blank temperature and furnace temperature target curves which accord with the current working conditions, and the blank temperature and furnace temperature target curves are compared with the actual measurement blank temperature and furnace temperature forecast curves of the current working conditions to obtain blank temperature and furnace temperature regulation and control deviation, and the blank temperature and furnace temperature regulation and control deviation is characterized by being controlled in the temperature field thermal balance relation and time lag, and the blank temperature and the furnace temperature regulation and control deviation are mutually coupled and relatively independent. And judging whether the temperature-rising curve requirements of the current hearth and the blank are met or not according to the root-mean-square deviation of the blank temperature and the furnace temperature respectively, and performing closed-loop regulation and control through self-adaption and self-learning. On the basis, the method considers the limited conditions or optimization targets of energy consumption, burning loss, pollutants and the like, and carries out progressive optimization, thereby obtaining stable furnace conditions. The coordinate of the length of the heating furnace is set as x, wherein,
the soft measurement forecast curve of the furnace temperature has the formula as follows:
T f ＝A+Bx+Cx 2 +Dx 3 +Ex 4 +Fx 5 (1)
the formula of the soft measurement forecast curve of the blank temperature is as follows:
T s ＝a+bx+cx 2 +dx 3 +ex 4 +fx 5 (2)
temperature of the blank
Furnace temperature
The standard curves are respectively represented as
Wherein ABCDEF and ABCDEF are furnace temperature and blank temperature target characteristic coefficients respectively. Setting the error characteristic values of the furnace temperature and the blank temperature of the current furnace condition as
And
respectively superposed to the standard curves, the current blank temperature and furnace temperature target curves can be expressed as
Comparing with the forecast curve of soft measurement, the actual furnace temperature T can be obtained f Actual blank temperature T s Respectively is
ΔT s ＝T s -T s + (8)
If n nodes are divided along the furnace length, the standard deviation sigma of the furnace temperature is f Standard deviation of blank temperature sigma s Are respectively represented as
Wherein, Delta T fi Is the ith point temperature value mu on the current furnace temperature curve f Is the average furnace temperature; delta T si Is the ith point temperature value mu on the current furnace temperature curve s Is the average furnace temperature;
setting weight factor xi of furnace temperature and blank temperature, then overall optimization function psi T Is shown as
ψ T ＝min[ξσ f +(1-ξ)σ s ] (11)
Based on the relational expression, the high-precision heating regulation control can be carried out on the whole furnace temperature and blank temperature conditions, so that the furnace conditions are strictly regulated and controlled according to a target curve, and finally the discharged blank temperature is ensured to meet the actual process requirements. Setting the surface temperature of the discharged blank as T sout The target value of the surface of the blank is
The temperature difference between the inside and the outside of the outlet blank is delta T sout Target deviation of
The weight factor zeta of the outlet billet temperature is the optimization function psi about the outlet billet temperature and the temperature difference between the inside and the outside of the outlet billet temperature Tout Is composed of
On the basis of finely controlling the furnace temperature and the blank temperature, in the actual production process, the energy consumption requirement and the environmental protection requirement are considered, the indexes of the fuel consumption, pollutant emission, burning loss and the like in unit time have to be considered, and the deviation between the fuel consumption, pollutant and burning loss in unit time and the target value is respectively set as delta G 1 、ΔG 2 And Δ G 3 Then the overall optimization objective can be expressed as
ψ＝w T ψ T +w Tout ψ Tout +w 1 ΔG 1 +w 2 ΔG 2 +w 3 ΔG 3 (13)
Wherein w T ，w Tout ，w 1 ，w 2 ，w 3 Are respectively corresponding weight factors, w T +w Tout +w 1 +w 2 +w 3 1, in the optimization process, iterative optimization calculation is sequentially carried out according to the sequence, so that multi-objective progressive optimization is realized, wherein the furnace temperature and the blank temperature are the first objectives, and the target curve indirectly contains indexes such as fuel consumption, burning loss, pollutant emission and the like, so that auxiliary optimization operation can be carried out in a progressive mode in the formula (13) to obtain the indexes which are regarded as constraint conditions, so that the furnace condition is better stabilized, and the control effect of each index is improved. When setting, the first target is taken as a main target, the weight factor is maximum, the subsequent targets are sequentially a second target, a third target, a fourth target and a fifth target, and the weight factor or the weight coefficient is respectively set to be the same or decreased progressively according to actual needs. In the regulation and control process, iteration optimization is carried out on a first target, and a set convergence condition (such as +/-5%) is met; secondly, performing iterative optimization of a second target on the basis of meeting the first target, and if the first target is out of limit in the iterative process, performing the first target optimization again; according to the progressive method, the rotation optimization of multiple targets is sequentially carried out, and iterative operation is carried out in real time, so that each target is ensured to reach the set optimal value range respectively. In the practical application process, the iteration sequence or the weight of multiple targets can be respectively adjusted according to the practical requirements, so that the optimal value of the target is ensured to the maximum extent.
FIG. 2 illustrates a technical implementation route for multi-objective intelligent setting. The Level1 system is used for online closed-loop regulation, and the Level2 system is used for combustion regulation and control, and is a known system of the existing heating furnace. Collecting working condition signals of the heating furnace such as furnace temperature, furnace pressure, flow and the like in real time, obtaining a temperature forecast curve of the whole furnace condition by utilizing a blank temperature and furnace temperature coupling temperature field model, carrying out high-precision soft measurement on the blank temperature and the furnace temperature by combining test verification of a black box, and carrying out online closed-loop measurement and control on the blank temperature and the furnace temperature as measured values. In the control process, a most approximate standard sample curve is called from the standard temperature-rising curve control, and the error of the current working condition is superposedAnd forming a target temperature rise curve by using the characteristic values (such as furnace pressure, air-fuel ratio fluctuation error and the like), and comparing the target temperature rise curve with the actual curves of the blank temperature and the furnace temperature to obtain a temperature deviation curve. On the basis, whether the temperature-rising curve is consistent with a target curve or not is judged according to the mean square deviation, if not, a Level1 system is utilized to carry out rapid real-time regulation and control, and the self-adaptive quantitative control of the furnace temperature is implemented by combining the air-fuel ratio double-crossing amplitude limiting rule, so that the blank temperature and the furnace temperature quickly reach a stable state and meet the set temperature target; if the temperature of the blank is consistent with the temperature of the steel, multi-objective weighted optimization is carried out on the steel, indexes such as energy consumption, burning loss, pollutant discharge and steel burning yield are considered, a Level2 system is used for carrying out long-term self-learning optimization regulation, and a coupled temperature field model is also used for carrying out fine adjustment on the blank temperature and the furnace temperature so as to make a better target temperature rising curve. On the basis of meeting the functions, all accidents or faults of the heating furnace are classified and threshold value set, such as indexes of temperature and temperature rise rate, air-fuel ratio, furnace pressure, fuel consumption, burning loss, pollutant emission and the like of each section of the heating furnace, a good stable state is combined with a weighted set value of a theoretical ideal value to serve as a standard threshold value, an unstable accident state is used as a standard sample of an early warning range, after data comparison with current working condition data is carried out, the type and degree of the accidents are judged, targeted regulation and control are carried out, and the accidents or faults are avoided to the maximum extent. The slight accident or early warning can be automatically processed by utilizing Level1 or Level2, the inevitable moderate accident or fault reminds people to participate in timely maintenance operation, and the sudden serious accident carries out early acousto-optic alarm, so that the emergency plan is started in time, and the heating furnace is prevented from being damaged by larger accidents or irreversible faults. Assuming by temperature
Early warning target, setting early warning upper and lower limits
Threshold value is set to
Taking this as an example, the blank temperature, the furnace temperature, the blank-out temperature, the internal and external temperature difference, the fuel consumption, the pollutant discharge, and the burning loss are preset respectively. And when the forecast value exceeds the corresponding preset set range, giving a corresponding alarm prompt and giving physical mechanism analysis and solution suggestions. In the practical application process, the whole furnace condition parameters are monitored in real time, online calculation is carried out by utilizing a mechanism-intelligent cooperative temperature field model, and possible reasons of accidents are judged according to a forecast curve, wherein slight fluctuation can be regarded as normal fluctuation within an neglected range; when the light degree is out of tolerance, sound and light alarm prompt is given in time, meanwhile, the Level1 and Level2 systems are used for automatically regulating and controlling processing in real time, and relevant processing processes are recorded, so that the historical records are conveniently inquired in the later period to find out the reason; when the furnace condition parameters fluctuate violently and medium faults occur, the furnace condition parameters exceed a closed-loop regulation or preset range, manual participation or manual observation is needed at the moment to analyze new working conditions or sudden working conditions, supplement new working condition parameters, and simultaneously establish a proper new target curve to deal with the faults or the new working conditions; when greater furnace condition fluctuation occurs and severe early warning occurs, potential hazard points which may cause problems, such as pressure, flow, leakage, pollutants, burning loss and other indexes are seriously out of tolerance, which indicates that accidents may occur in hardware of the heating furnace, and at the moment, personnel are required to be arranged in time to maintain, overhaul or replace parts, so that early prevention and early treatment are realized, and major accidents are avoided.
Fig. 3 shows a basic control sequence of the furnace temperature and the billet temperature of the heating furnace. In the traditional regulation and control process, the uncertainty of manual regulation and control is controlled, and in order to ensure that the temperature of the outlet billet meets the index requirement, most of people focus on the regulation and control of a soaking section and try to control the temperature of the billet in a set value range in the soaking section. However, in the manual mode, the time lag and the disturbance of the heat balance in the furnace are difficult to consider to a great extent, and the manual mode is not easy to adapt to the temperature deviation caused by the complicated furnace atmosphere fluctuation such as the furnace pressure, the air-fuel flow, the feeding rhythm and the like, so that the regulation and control effect is often unsatisfactory. Even if a Level2 system is adopted, the real-time states of the furnace temperature and the blank temperature can not be accurately calculated due to poor model precision and low coupling degree, so that the false secondary cognition can be caused, the complex working condition change can not be adapted, even the manual regulation and control efficiency and precision are not good, and finally the situation that the situation is abandoned is caused, and the situation becomes a chicken rib or only the functions of data communication and statistics are achieved. Based on the problems, high-precision prediction of the blank temperature and the furnace temperature needs to be guaranteed, the blank temperature and the furnace temperature need to be subjected to coupling calculation under the same working condition, and high-precision temperature prediction is obtained by combining the regularity pre-judgment of a mechanism model and utilizing the process error compensation function of large data measured on site and mechanism-intelligent cooperative regulation; secondly, after a high-precision furnace temperature and blank temperature forecast curve is obtained, a Level1 system is used for preferentially adjusting a preheating section according to the temperature deviation between each section of the heating furnace and a target curve, so that the preheating section is enabled to be in accordance with a temperature rise rule curve of the preheating section as much as possible, the rapid temperature rise rule of the heating section is enabled to meet a set temperature rise curve, a foundation is laid for fine furnace temperature and blank temperature regulation and control of a soaking section, blank temperature uniformity and blank discharging temperature precision of the soaking section are finally guaranteed, the whole temperature regulation process is in accordance with a physical mechanism, quantitative regulation and control of the furnace temperature and the blank temperature can be realized, stable rolling rhythm is facilitated, large-amplitude furnace temperature regulation is reduced, frequent fluctuation of furnace atmosphere is avoided, and the purposes of accurately controlling the furnace temperature, saving energy and reducing consumption are achieved. In addition, by means of quantitative regulation and control of the whole furnace condition and threshold value early warning, the combustion state of the whole heating furnace can be monitored and accident prejudged to a great extent in real time, once parameters are abnormal or regular jumping occurs, the parameters can be found, early warning and processing can be carried out timely, automatic and manual regulation is carried out through a Level2 system, safety and stability of the heating furnace are guaranteed to the maximum extent, the temperature of the furnace and blank can be regulated flexibly according to changes of rolling rhythm, the current thermal balance state is switched to the thermal balance state of another working condition steadily, and full-automatic careless transition or intelligent regulation and control are achieved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. A multi-objective progressive optimization method for a heating furnace is characterized by comprising the following steps:
establishing a furnace temperature and blank temperature synchronous coupling mechanism model under the same working condition;
acquiring a blank temperature soft measurement forecasting curve and an oven temperature soft measurement forecasting curve based on the oven temperature and blank temperature synchronous coupling mechanism model;
acquiring a furnace temperature standard deviation curve and a blank temperature standard deviation curve based on the blank temperature soft measurement forecast curve and the furnace temperature soft measurement forecast curve;
taking the furnace temperature standard difference curve and the blank temperature standard difference curve as convergence conditions, firstly performing coupled calculation of the furnace temperature blank temperature, and synchronously iterating to obtain a forecast curve of the optimal blank temperature;
setting the outlet blank temperature and the internal and external temperature difference as first optimization conditions in the optimal blank temperature furnace temperature prediction curve to ensure that the outlet blank temperature always meets a target set value;
setting different weighting factors according to target requirements, setting the deviation and the weighting factor of the fuel consumption and the target value in unit time, setting the deviation and the weighting factor of pollutants and the target value, and setting the deviation and the weighting factor of the burning loss and the target value;
substituting the deviation and the weight factor of the fuel consumption and the target value in unit time, the deviation and the weight factor of the pollutant and the target value, and the deviation and the weight factor of the set burning loss and the target value into a synchronous coupling mechanism model of the blank temperature furnace temperature to obtain a multi-objective optimization function;
carrying out progressive iteration solution on the multi-objective optimization function;
regulating and controlling the air-fuel ratio in the furnace to make the furnace temperature curve and the blank temperature curve respectively consistent with the furnace temperature target curve and the blank temperature target curve under the current working condition;
on the basis of meeting the temperature control precision of the furnace temperature and the blank temperature, optimizing and controlling the natural gas consumption in unit time to be minimum, and simultaneously ensuring that the temperature curves of the furnace temperature and the blank temperature are consistent with the blank temperature target curve under the current working condition and the furnace temperature target curve under the current working condition;
on the basis of meeting the minimum requirements of furnace temperature, blank temperature control precision and natural gas fuel consumption, the pollutants are optimally regulated and controlled to be minimum, so that the pollutant discharge amount reaches the specified standard;
on the basis of meeting the requirements of furnace temperature, billet temperature control precision, minimum natural gas burnup and minimum regulation and control pollutants, the heating time of the billet is optimally regulated and controlled, so that the burning loss of the billet reaches the specified standard.
2. The heating furnace multi-objective progressive optimization method according to claim 1, wherein the furnace temperature and blank temperature synchronous coupling mechanism model is established by the following steps:
coupling environment of the virtual heating furnace and the blank;
the hearth is regarded as a physical body to carry out dynamic field quantity modeling;
setting the temperature of the hearth and the temperature of the blank as boundary conditions;
and obtaining a furnace temperature and blank temperature synchronous coupling mechanism model.
3. The heating furnace multi-objective progressive optimization method according to claim 1, wherein the error characteristic values of the current furnace conditions include furnace pressure fluctuation, air/gas flow and pressure fluctuation, and residual oxygen amount.
4. The heating furnace multi-objective progressive optimization method according to claim 1,
the soft measurement forecast curve of the furnace temperature has the formula as follows:
T f ＝A+Bx+Cx 2 +Dx 3 +Ex 4 +Fx 5
the formula of the soft measurement forecast curve of the blank temperature is as follows:
T s ＝a+bx+cx 2 +dx 3 +ex 4 +fx 5
the formula of the furnace temperature standard curve is as follows:
the formula of the blank temperature standard curve is as follows:
wherein x is a heating furnace length coordinate, A, B, C, D, E, F is a furnace temperature target characteristic coefficient, and a, b, c, d, e and f are blank temperature target characteristic coefficients of each order;
the formula of the furnace temperature target curve of the current working condition is as follows:
the formula of the blank temperature target curve under the current working condition is as follows:
T s + ＝T s * +T s -
wherein, T s - Is the furnace temperature error characteristic value of the current furnace condition,
the characteristic value of the blank temperature error of the current furnace condition is obtained;
the formula of the furnace temperature regulation deviation curve is as follows:
the formula of the embryo temperature regulation deviation curve is as follows:
ΔT s ＝T s -T s +
wherein, T f Is the actual furnace temperature, T s The actual blank temperature is obtained;
setting n nodes divided along the furnace length, wherein the formula of the furnace temperature standard deviation curve is as follows:
wherein, Δ T fi Is the ith point temperature value, mu, on the current furnace temperature curve f Is the average furnace temperature;
the formula of the blank temperature standard deviation curve is as follows:
wherein, Delta T si Is the ith point temperature value mu on the current furnace temperature curve s Is the average furnace temperature;
setting a weight factor xi of the furnace temperature and the blank temperature, wherein a formula of a forecast curve of the optimal blank temperature furnace temperature is as follows:
ψ T ＝min[ξσ f +(1-ξ)σ s ]
the optimization function of the outlet blank temperature and the internal and external temperature difference is as follows:
wherein, T sout Is the surface temperature of the furnace blank,
as a blank surface target value, Δ T sout Is the temperature difference between the inside and the outside of the outlet blank,
zeta outlet blank temperature weight factor for target deviation;
the multi-objective optimization function is as follows:
ψ＝w T ψ T +w Tout ψ Tout +w 1 ΔG 1 +w 2 ΔG 2 +w 3 ΔG 3
wherein, Δ G 1 Deviation of burnup per unit time from a target value, Δ G 2 Δ G deviation of the contamination from the target value 3 Deviation of burn-out from target value, w 1 Is a weight factor of burnup per unit time, w 2 Is a weight factor of the contaminant, w 3 Weight factor for burn-up, w T Weight factor, w, of a blank furnace temperature coupling optimization function Tout And the weight factor of the outlet billet temperature optimization function.
5. The heating furnace multi-objective progressive optimization method according to claim 1, wherein in the step of performing progressive iterative solution on the multi-objective optimization function, the furnace temperature and the blank temperature are a first objective, the productivity is a second objective, the fuel consumption per unit time is a third objective, the pollutant is a fourth objective, the burning loss is a fifth objective, and the weighting factors of the first objective, the second objective, the third objective, the fourth objective and the fifth objective are sequentially decreased; or adjusting the sequence or weight factor proportion of the optimization target according to the actual requirements of the site.
6. The heating furnace multi-objective progressive optimization method according to claim 5, wherein in the step of performing progressive iterative solution on the multi-objective optimization function, iterative optimization is performed on a first objective first, and iterative optimization is performed on a second objective under the condition that the first objective is met; if the first target is out of limit in the iteration process, the first target iteration optimization is carried out again; and if the first target does not exceed the limit, performing third target iterative optimization until the fifth target iterative optimization is completed.
7. A multi-target progressive early warning method for a heating furnace is realized based on any one of the heating furnace multi-target progressive optimization methods in claims 1-6, and is characterized by comprising the following steps:
collecting working condition signals of the heating furnace in real time;
bringing the working condition signal of the heating furnace into the furnace temperature and blank temperature synchronous coupling mechanism model to obtain a temperature prediction curve of the whole furnace condition;
carrying out black box test verification on the temperature prediction curve of the whole furnace condition, optimizing the actual boundary condition of the model, improving the prediction precision, carrying out high-precision soft measurement on the blank temperature and the furnace temperature, and simultaneously obtaining a standard temperature rise curve set;
centrally selecting a curve with the minimum temperature difference between the discharged blank and the inside and the outside as a standard temperature rise curve from the standard temperature rise curve, and superposing the standard temperature rise curve on the error characteristic value of the current working condition to obtain a target temperature rise curve;
subtracting the actual curves of the blank temperature and the furnace temperature from the target temperature rise curve to obtain a temperature deviation curve;
judging whether the predicted temperature-rise curve is matched with a target temperature-rise curve or not according to the mean square deviation of the temperature deviation curve, if not, utilizing a Level1 regulation system to carry out real-time regulation and control, combining an air-fuel ratio double-crossing amplitude limiting rule, and implementing self-adaptive quantitative control of the furnace temperature, so that the blank temperature and the furnace temperature quickly reach a stable state and meet a set temperature target; when the target temperature rise curves are matched, multi-target weighted optimization is carried out on the target temperature rise curves, productivity, energy consumption, burning loss and pollutant emission indexes are considered, a Level2 combustion control system is used for carrying out self-learning optimization regulation and control, and a more optimal target temperature rise curve is made.
8. The heating furnace multi-target progressive early warning method according to claim 7, wherein judging whether a forecast temperature rise curve of black box actual measurement or model soft measurement is consistent with a target temperature rise curve comprises the following steps:
classifying all accidents or faults of the heating furnace and setting a threshold value, taking a good stable state and a weighted set value of a theoretical value as a standard threshold value, taking an unstable accident state as a standard sample of an early warning range, and judging the type and degree of the accident after data comparison is carried out on current working condition data and the standard threshold value;
when a slight accident happens, a Level1 regulation and control system or a Level2 combustion control system is adopted for automatic processing;
when a moderate accident occurs, reminding people to participate in maintenance operation;
when a serious accident occurs, sound and light alarm is carried out in advance, and an emergency plan is started;
the standard threshold is as follows:
wherein:
in order to pre-alarm the target for the temperature,
in order to provide an upper warning limit,
the lower limit of the early warning is set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210779367.2A CN115096106B (en) | 2022-07-01 | 2022-07-01 | Multi-target progressive optimization and early warning method for heating furnace |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210779367.2A CN115096106B (en) | 2022-07-01 | 2022-07-01 | Multi-target progressive optimization and early warning method for heating furnace |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115096106A true CN115096106A (en) | 2022-09-23 |
| CN115096106B CN115096106B (en) | 2023-04-18 |
Family
ID=83294675
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210779367.2A Active CN115096106B (en) | 2022-07-01 | 2022-07-01 | Multi-target progressive optimization and early warning method for heating furnace |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115096106B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882158A (en) * | 2023-07-06 | 2023-10-13 | 昆明理工大学 | Engineering design-oriented piston-ring set-cylinder structure collaborative optimization design method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101806541A (en) * | 2010-04-09 | 2010-08-18 | 首钢总公司 | Model for optimally controlling heating system of large walking beam type heating furnace plate blank |
| CN106636610A (en) * | 2016-11-25 | 2017-05-10 | 浙江中控研究院有限公司 | Time-and-furnace-length-based double-dimensional stepping type heating curve optimizing setting method of heating furnace |
| CN106906351A (en) * | 2017-02-10 | 2017-06-30 | 中冶华天南京工程技术有限公司 | A kind of board briquette forecasting model and optimum furnace method |
| CN113295016A (en) * | 2021-06-04 | 2021-08-24 | 安徽淮光智能科技有限公司 | Heating furnace combustion control method based on temperature measurement curve |
| CN216486062U (en) * | 2021-12-01 | 2022-05-10 | 鑫鹏源(聊城)智能科技有限公司 | Device for online intelligent detection and optimal control system of combustion efficiency of heating furnace |
-
2022
- 2022-07-01 CN CN202210779367.2A patent/CN115096106B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101806541A (en) * | 2010-04-09 | 2010-08-18 | 首钢总公司 | Model for optimally controlling heating system of large walking beam type heating furnace plate blank |
| CN106636610A (en) * | 2016-11-25 | 2017-05-10 | 浙江中控研究院有限公司 | Time-and-furnace-length-based double-dimensional stepping type heating curve optimizing setting method of heating furnace |
| CN106906351A (en) * | 2017-02-10 | 2017-06-30 | 中冶华天南京工程技术有限公司 | A kind of board briquette forecasting model and optimum furnace method |
| CN113295016A (en) * | 2021-06-04 | 2021-08-24 | 安徽淮光智能科技有限公司 | Heating furnace combustion control method based on temperature measurement curve |
| CN216486062U (en) * | 2021-12-01 | 2022-05-10 | 鑫鹏源(聊城)智能科技有限公司 | Device for online intelligent detection and optimal control system of combustion efficiency of heating furnace |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882158A (en) * | 2023-07-06 | 2023-10-13 | 昆明理工大学 | Engineering design-oriented piston-ring set-cylinder structure collaborative optimization design method |
| CN116882158B (en) * | 2023-07-06 | 2024-03-26 | 昆明理工大学 | Engineering design-oriented piston-ring set-cylinder structure collaborative optimization design method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115096106B (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115096106B (en) | Multi-target progressive optimization and early warning method for heating furnace | |
| CN103148473B (en) | Optimal operation method and system for utility boiler based on CO | |
| CN104750131B (en) | A kind of fluidized-bed temperature control method based on parameter identification | |
| CN109492335B (en) | Method and system for predicting furnace temperature of annealing furnace | |
| CN103170508B (en) | Method for controlling width of hot rolling strip steel | |
| CN105005632B (en) | The blast furnace crucible corrosion Forecasting Methodology of multiple layer refractory tile stove wall construction | |
| CN110184445B (en) | Test method of heating furnace test device | |
| CN102886383A (en) | Method for controlling mechanical properties of cold-rolling strip steel in online manner | |
| CN110066895A (en) | A kind of blast-melted quality section prediction technique based on Stacking | |
| CN101881981A (en) | Closed loop control system for temperature and components of RH (Rockwell Hardness) molten steel | |
| CN105868165B (en) | A kind of station boiler operation data cleaning method | |
| CN100465294C (en) | Intelligent control method for bottom-blowing argon in refining furnace | |
| CN104894362A (en) | Method for setting temperature of heating furnace in cold and hot steel billet mixed loading | |
| CN110438331A (en) | A kind of heating furnace autocombustion system | |
| CN109710976A (en) | A kind of heater for rolling steel gas consumption amount prediction technique | |
| CN117007128A (en) | Aluminum alloy production on-line monitoring system for automobile | |
| CN108984943B (en) | Heating furnace billet temperature tracking model correction method | |
| CN104561514B (en) | Double heat accumulating type steel rolling heating furnace oxidizing atmosphere adjustment method and automatic control method thereof | |
| CN109654897B (en) | Intelligent sintering end point control method for improving carbon efficiency | |
| CN110656234A (en) | Automatic distinguishing control method for maximum heating temperature of steel rolling heating furnace | |
| CN103699782B (en) | Coal feeding amount soft measuring method of middle-speed powder milling and preparation system | |
| CN115287446A (en) | Long-range big data centralized control system of wisdom heating furnace | |
| CN111998383B (en) | Over-fire air control method based on boiler load and flame central point coordinate quantification | |
| CN109055640B (en) | Method and device for accurately controlling temperature of furnace burning flue of blast furnace hot blast stove | |
| CN104281054A (en) | Intelligent fuzzy control method for total hydrogen bell furnace heating process |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |