CN113108614B - Method and device for controlling reaction furnace, medium and electronic equipment - Google Patents

Method and device for controlling reaction furnace, medium and electronic equipment Download PDF

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CN113108614B
CN113108614B CN202110410475.8A CN202110410475A CN113108614B CN 113108614 B CN113108614 B CN 113108614B CN 202110410475 A CN202110410475 A CN 202110410475A CN 113108614 B CN113108614 B CN 113108614B
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李刚
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China ENFI Engineering Corp
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    • 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
    • F27D19/00—Arrangements of controlling devices
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
    • C22B4/00—Electrothermal treatment of ores or metallurgical products for obtaining metals or alloys
    • C22B4/08—Apparatus
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

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Abstract

The disclosure provides a method, a device, a medium and an electronic apparatus for controlling a reaction furnace. The control method of the reaction furnace comprises the following steps: determining the reaction heat of the material according to the input electric energy, the feeding heat and the loss heat of the reaction furnace; determining the reaction product amount in the reaction furnace according to the reaction heat; determining the slag proportion in the reaction product amount to determine the slag amount and/or the discharge amount; controlling the slag discharge process of the reaction furnace according to the slag discharge quantity, and/or controlling the discharge process of the reaction furnace according to the discharge quantity. Through the technical scheme provided by the embodiment of the disclosure, the control efficiency and the control accuracy of the deslagging process or the discharging process are improved, the energy efficiency utilization rate is improved, the product quality is also improved, and the energy consumption cost is further reduced.

Description

Method and device for controlling reaction furnace, medium and electronic equipment
Technical Field
The disclosure relates to the technical field of smelting, and particularly relates to a control method of a reaction furnace, a control device of the reaction furnace, a computer readable storage medium and an electronic device.
Background
The ore-smelting electric furnace is used as an important reaction furnace for smelting, and the purpose of smelting is achieved by loading current to materials by using an electrode to generate heat. The ore-smelting electric furnace receives hot materials generated by an upstream process flow, and converts electric energy into heat energy to heat the materials, so that the materials are melted, reacted and separated in the furnace. As the reaction process in the thermoelectric furnace proceeds, a slag layer and a metal layer are gradually generated in the furnace. Therefore, the accurate layering height in the ore-smelting electric furnace can be obtained, and the method is particularly important for controlling the slag discharge process and the material discharge process in time.
In the related art, a commonly adopted method for judging the height of a slag layer or the height of a metal layer is as follows: and measuring and judging the height of the slag layer or the height of the metal layer according to the ablation traces attached to the steel chisel inserted into the furnace and pulled out. Because the method is limited by the operation frequency, the operation experience of operators and the like, the method is difficult to detect the layering in the furnace in real time, thereby causing the problem of untimely slag discharge or material discharge, reducing the energy efficiency utilization rate and reducing the product quality.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method for controlling a reaction furnace, a control apparatus for a reaction furnace, a medium, and an electronic device, which are used to overcome, at least to some extent, the problem of low control efficiency of slag discharge or discharge due to the limitations and disadvantages of the related art.
According to an aspect of the embodiments of the present disclosure, there is provided a method of controlling a reaction furnace, including: determining the reaction heat of the material according to the input electric energy, the feeding heat and the loss heat of the reaction furnace; determining the amount of reaction products in the reaction furnace according to the reaction heat; determining the slag proportion in the reaction product amount to determine the slag amount and/or the discharge amount; controlling the slag discharge process of the reaction furnace according to the slag discharge quantity, and/or controlling the discharge process of the reaction furnace according to the discharge quantity.
In an exemplary embodiment of the present disclosure, determining the reaction heat of the material according to the input electric energy, the input heat and the loss heat of the reaction furnace comprises: determining the input electric energy of the reaction furnace; and/or determining the feeding heat according to the feeding amount, the feeding temperature and the heat parameters of the materials in the reaction furnace; and/or determining the loss of heat according to the structure of the reaction furnace; and determining the reaction heat of the material according to the input electric energy of the reaction furnace, the feeding heat and the loss heat of the reaction furnace.
In an exemplary embodiment of the present disclosure, determining the amount of the reaction product inside the reaction furnace according to the reaction heat includes: determining reaction parameters of reaction products in the reaction furnace; the amount of the reaction product is determined based on the heat of reaction and the reaction parameters.
In an exemplary embodiment of the present disclosure, the method of controlling a reaction furnace further includes: determining an input electric energy sample and a feed heat sample for training a neural network model; determining a lost heat sample of the reaction furnace; determining a slag quantity sample and/or a discharge quantity sample according to the discharge history of the reaction furnace; and taking the feeding heat sample, the input electric energy sample and the loss heat sample as input samples of the neural network model, and taking the slag discharge sample and/or the discharge sample as output results of the neural network model, and training the neural network model.
In an exemplary embodiment of the present disclosure, the neural network model includes a first sub-model, the training of the neural network model with the input heat sample, the input electric energy sample and the loss heat sample as input samples of the neural network model and with the slag sample and/or the discharge sample as output results of the neural network model includes: binding a feeding heat sample and an input electric energy sample into a first sample; and taking the first sample and the lost heat sample as input samples of the first submodel, taking the slag output sample and/or the discharge sample as output results of the first submodel, and training the neural network model.
In an exemplary embodiment of the present disclosure, the neural network model includes a second sub-model, the input samples of the neural network model are input samples of the input heat samples, the input electric energy samples and the loss heat samples, and the output results of the neural network model are output samples of the slag output samples and/or the discharge output samples, and the training of the neural network model further includes: binding the feeding heat sample and the loss heat sample into a second sample; and taking the second sample and the input electric energy sample as input samples of the second submodel, taking the slag output sample and/or the discharge sample as output results of the second submodel, and training the neural network model.
In an exemplary embodiment of the present disclosure, the neural network model includes a third sub-model, the training of the neural network model with the input heat sample, the input electric energy sample and the loss heat sample as input samples of the neural network model and with the slag sample and/or the discharge sample as output results of the neural network model further includes: binding the lost heat sample and the input electrical energy sample into a third sample; and taking the third sample and the feeding heat sample as input samples of a third submodel, taking the slag output sample and/or the discharging amount sample as output results of the third submodel, and training the neural network model.
In an exemplary embodiment of the present disclosure, controlling a slag discharging process of the reaction furnace according to a slag discharge amount, and/or controlling a discharging process of the reaction furnace according to a discharge amount includes: determining the height of a predicted melting layer according to the sectional area of a hearth of the reaction furnace, the slag density and the slag quantity; and/or determining the height of a predicted melting layer according to the hearth sectional area, the discharging density and the discharging amount of the reaction furnace; and discharging the discharged materials and/or the discharged slag in the reaction furnace if the predicted molten layer height meets the discharge condition.
According to another aspect of the embodiments of the present disclosure, there is provided a control apparatus of a reaction furnace, including: the determining module is used for determining the reaction heat of the material according to the input electric energy, the feeding heat and the loss heat of the reaction furnace; the determining module is also used for determining the reaction product amount in the reaction furnace according to the reaction heat; the determining module is also used for determining the slag proportion in the reaction product amount so as to determine the slag amount and/or the discharge amount; and the control module is used for controlling the slag discharge process of the reaction furnace according to the slag discharge quantity and/or controlling the discharge process of the reaction furnace according to the discharge quantity.
According to another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the control method of the reaction furnace as in any one of the above.
According to another aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to execute the method of controlling the reaction furnace as in any one of the above via execution of the executable instructions.
According to the technical scheme of the embodiment of the disclosure, the feeding amount, the feeding temperature, the flue gas temperature and the flue gas flow of the reaction furnace are detected, the reaction product amount is determined through the reaction heat, the slag layer height and the metal layer height are calculated in real time through the slag charge proportion, the calculation result is corrected by combining a neural network model in the real-time calculation process, and finally the slag discharge process or the material discharge process of the reaction furnace is controlled according to the calculation result, so that the control efficiency and the control accuracy of the slag discharge process or the material discharge process are improved, the energy efficiency utilization rate is improved, the product quality is also improved, and the energy consumption cost is further reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It should be apparent that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived by those of ordinary skill in the art without inventive effort.
FIG. 1 illustrates a flow chart of a method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 2 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 3 shows a flow chart of another method of controlling a reactor in an exemplary embodiment of the present disclosure;
FIG. 4 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 5 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 6 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 7 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 8 shows a flow chart of another method of controlling a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 9 shows a schematic structural view of a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 10 shows a schematic diagram of a neural network model of a reactor in an exemplary embodiment of the present disclosure;
FIG. 11 shows a schematic diagram of a control system of a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 12 shows a block diagram of a control apparatus of a reaction furnace in an exemplary embodiment of the present disclosure;
FIG. 13 shows a block diagram of an electronic device in an exemplary embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate a number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "plurality" means at least two, e.g., two, three, etc., unless explicitly defined otherwise. The symbol "/" generally indicates that the former and latter associated objects are in an "or" relationship.
In this disclosure, unless expressly stated or limited otherwise, the terms "connected" and the like are to be construed broadly, e.g., can be electrically connected or can communicate with one another; may be directly connected or indirectly connected through an intermediate. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.
Further, the drawings are merely schematic illustrations of the present disclosure, in which the same reference numerals denote the same or similar parts, and thus, a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The following detailed description of exemplary embodiments of the disclosure refers to the accompanying drawings.
Fig. 1 is a flowchart of a control method of a reaction furnace in an exemplary embodiment of the present disclosure.
Referring to fig. 1, a method of controlling a reaction furnace includes:
and S102, determining the reaction heat of the material according to the input electric energy, the feeding heat and the loss heat of the reaction furnace.
In an exemplary embodiment of the present disclosure, the material includes material added to a reaction furnace. The input electric energy of the reaction furnace is converted into heat energy to heat the materials in the furnace, and the sum of the heat absorbed by the materials when the materials are melted in the furnace and the heat generated when the materials react in the furnace is the reaction heat of the materials.
And step S104, determining the reaction product amount in the reaction furnace according to the reaction heat.
And step S106, determining the slag proportion in the reaction product amount to determine the slag amount and/or the discharge amount.
In an exemplary embodiment of the present disclosure, the reaction product amount includes a discharge amount and a slag amount generated after the materials are melted and chemically reacted in the reaction furnace.
In an exemplary embodiment of the present disclosure, the slag proportion is determined by atomic conservation and reaction in the reaction furnace, and includes a ratio of a discharged weight to a material weight generated by melting the material per unit mass, and a ratio of a discharged weight to a material weight generated by melting the material per unit mass.
In an exemplary embodiment of the present disclosure, at least one of the slag or the tap amount in the reaction product amount may be determined according to a product of the reaction product amount and a slag proportion in the reaction product amount.
And S108, controlling the slag discharge process of the reaction furnace according to the slag discharge amount and/or controlling the discharge process of the reaction furnace according to the discharge amount.
In the above embodiment, the reaction heat of the material is determined according to the input electric energy, the feeding heat and the loss heat of the reaction furnace, the reaction product amount in the reaction furnace is determined according to the reaction heat, the slag proportion in the reaction product amount is determined to determine the slag amount and/or the discharge amount, the slag discharge process of the reaction furnace is controlled according to the slag discharge amount, or the discharge process of the reaction furnace is controlled according to the discharge amount, so that the control efficiency and the control accuracy of the slag discharge process or the discharge process are improved, the product quality is also improved, the energy efficiency utilization rate is improved, and the energy consumption cost is further reduced.
As shown in fig. 2, determining the reaction heat of the material according to the input electric energy, the input heat and the loss heat of the reaction furnace includes:
step S202, determining the input electric energy of the reaction furnace.
And S204, determining the feeding heat according to the feeding amount, the feeding temperature and the heat parameters of the materials of the reaction furnace.
In an exemplary embodiment of the present disclosure, the heat of the material input is determined according to the parameters of the material input, the material input temperature and the heat of the material of the reaction furnace, i.e. the theoretical value of the heat of the material input is calculated according to the parameters of the material input, the material input temperature and the heat of the material of the reaction furnace, and the material input is mass, volume, flow or volume, etc., but is not limited thereto.
In an exemplary embodiment of the disclosure, the heat parameter includes a specific heat capacity, and when the temperature of the material is changed and the temperature change value is known, the heat of the material is determined according to a product obtained by multiplying the temperature change value, the material feeding amount of the material and the specific heat capacity of the material.
In an exemplary embodiment of the present disclosure, the heat parameter includes a calorific value, and the feed heat of the material is determined according to a product obtained by multiplying the calorific value of the material by the feed amount of the material when the feed temperature is constant.
In step S206, the heat loss is determined according to the structure of the reaction furnace.
In an exemplary embodiment of the present disclosure, the structure of the reaction furnace includes a material inlet, a reaction electrode, a furnace cover, a furnace lining, a furnace wall, and a flue gas port. The feeding port is used for providing an inlet for adding materials into the reaction furnace, and the reaction electrode is used for providing input electric energy. The furnace cover, the furnace lining and the furnace wall form a furnace body of the reaction furnace, the furnace body is used for providing a space for melting materials, and the heat lost through the heat conduction of the furnace body is the heat lost by the furnace body. The smoke port is used for discharging high-temperature smoke generated in melting and reaction of materials. Wherein, the loss heat of reacting furnace includes the heat that high temperature flue gas carried, other stray loss heat that furnace body lost heat and the thermal radiation of reacting furnace lead to.
And S208, determining the reaction heat of the material according to the input electric energy of the reaction furnace, the feeding heat and the loss heat of the reaction furnace.
In the above embodiment, the input electric energy of the reaction furnace is determined, or the feed heat is determined according to the feed quantity, the feed temperature and the heat parameters of the material of the reaction furnace, or the loss heat is determined according to the structure of the reaction furnace, so that the reaction heat of the material is determined according to the input electric energy of the reaction furnace, the feed heat of the reaction furnace and the loss heat, the accuracy and the reliability of the control method of the reaction furnace are improved, and the control efficiency of the reaction furnace is improved.
As shown in fig. 3, the determining the amount of the reaction product in the reaction furnace according to the reaction heat includes:
step S302, determining reaction parameters of reaction products in the reaction furnace.
In step S304, the reaction product amount is determined according to the reaction heat and the reaction parameters.
In the embodiment, the reaction parameters of the reaction products in the reaction furnace are determined, and the reaction product amount is determined according to the reaction heat and the reaction parameters, so that the accuracy and the reliability of the control method of the reaction furnace are improved.
In an exemplary embodiment of the present disclosure, the reaction parameter of the reaction product includes a heating value, and the reaction product amount is determined by using a quotient obtained by dividing the reaction heat by the reaction parameter.
As shown in fig. 4, the method for controlling the reaction furnace includes:
step S402, input electric energy samples and feeding heat samples used for training the neural network model are determined.
Step S404, determining a loss heat sample of the reaction furnace.
And S406, determining a slag quantity sample and/or a discharge quantity sample according to the discharge history of the reaction furnace.
In an exemplary embodiment of the disclosure, the tapping history of the reaction furnace comprises a tapping history and a discharging history, and the slag quantity sample is determined according to the tapping quantity of the tapping history and/or the material quantity sample is determined according to the discharging quantity of the discharging history.
And step S408, taking the feeding heat sample, the input electric energy sample and the loss heat sample as input samples of the neural network model, taking the slag output sample and/or the discharge output sample as output results of the neural network model, and training the neural network model.
In the embodiment, the input electric energy sample and the feeding heat sample used for training the neural network model are determined, the lost heat sample of the reaction furnace is determined, the slag quantity sample and/or the discharging quantity sample are determined according to the furnace discharging history of the reaction furnace, the feeding heat sample, the input electric energy sample and the lost heat sample are used as the input sample of the neural network model, the slag quantity sample and/or the discharging quantity sample are used as the output result of the neural network model, the neural network model is trained, the number of the input samples is increased, the reliability of model training is improved, and the reliability and the accuracy of the control method of the reaction furnace are improved.
As shown in fig. 5, the neural network model includes a first sub-model, the training of the neural network model with the input heat sample, the input electric energy sample and the lost heat sample as the input samples of the neural network model and with the output result of the slag sample and/or the discharge sample as the output result of the neural network model includes:
step S502, binding the feeding heat sample and the input electric energy sample into a first sample.
And S504, taking the first sample and the lost heat sample as input samples of a first submodel, taking the slag output sample and/or the discharge output sample as output results of the first submodel, and training a neural network model.
In the embodiment, the feeding heat sample and the input electric energy sample are bound into the first sample, the first sample and the loss heat sample are used as the input samples of the first submodel, the slag discharge amount sample and/or the discharge amount sample are used as the output results of the first submodel, the neural network model is trained, the diversity of the input samples is improved, the accuracy of the output results of the model is improved, and the accuracy of the control method of the reaction furnace is improved.
As shown in fig. 6, the neural network model includes a second sub-model, the input heat sample, the input electric energy sample and the lost heat sample are used as the input samples of the neural network model, the output result of the neural network model is the slag output sample and/or the discharge output sample, and the training of the neural network model further includes:
Step S602, the feeding heat sample and the loss heat sample are bound as a second sample.
And step S604, taking the second sample and the input electric energy sample as input samples of a second submodel, taking the slag output sample and/or the discharge output sample as output results of the second submodel, and training a neural network model.
In the embodiment, the feeding heat sample and the loss heat sample are bound into the second sample, the second sample and the input electric energy sample are used as the input sample of the second submodel, the slag output sample and/or the discharge output sample are used as the output result of the second submodel, the neural network model is trained, the richness of the input sample is improved, the reliability of the model training is improved, and the reliability of the control method of the reaction furnace is improved.
As shown in fig. 7, the neural network model includes a third sub-model, the input heat sample, the input electric energy sample and the lost heat sample are used as the input samples of the neural network model, the output result of the neural network model is the slag output sample and/or the discharge output sample, and the training of the neural network model further includes:
step S702, the lost heat sample and the input electric energy sample are bound as a third sample.
And step S704, taking the third sample and the feeding heat sample as input samples of a third submodel, taking the slag output sample and/or the discharging amount sample as output results of the third submodel, and training a neural network model.
In the embodiment, the lost heat sample and the input electric energy sample are bound into the third sample, the third sample and the fed heat sample are used as the input samples of the third submodel, the slag discharge sample and/or the discharge sample are used as the output results of the third submodel, the neural network model is trained, the combination form of the input samples is enriched, the model prediction accuracy is improved, and the reliability and the accuracy of the control method of the reaction furnace are improved.
As shown in fig. 8, controlling the slag discharging process of the reactor according to the slag discharging amount, and/or controlling the discharging process of the reactor according to the discharging amount includes:
and S802, determining the height of the predicted molten layer according to the sectional area of the hearth of the reaction furnace, the slag density and the slag quantity.
In an exemplary embodiment of the present disclosure, the predicted melt layer height is determined by dividing a slag volume by a slag density of the reaction furnace, and using a quotient of the slag volume divided by a furnace sectional area of the reaction furnace.
And step S804, determining the height of the predicted molten layer according to the sectional area of the hearth of the reaction furnace, the discharging density and the discharging amount.
In an exemplary embodiment of the present disclosure, the predicted melt layer height is determined by dividing a discharge volume by a discharge density of the reaction furnace, and using a quotient of the discharge volume divided by a furnace sectional area of the reaction furnace.
And step S806, discharging and/or deslagging in the reaction furnace if the predicted melting layer height meets the discharge condition.
In the above embodiment, the predicted molten layer height is determined according to the furnace sectional area, the slag density and the slag amount of the reaction furnace, and if the predicted molten layer height meets the discharge condition, the slag in the reaction furnace is discharged, or the predicted molten layer height is determined according to the furnace sectional area, the discharge density and the discharge amount of the reaction furnace, and if the predicted molten layer height meets the discharge condition, the discharge in the reaction furnace is discharged, or the predicted molten layer height is determined according to the furnace sectional area, the slag density, the discharge density and the slag amount of the reaction furnace, and if the predicted molten layer height meets the discharge condition, the discharge and the slag in the reaction furnace are discharged, so that the prediction accuracy of the molten layer height is improved, the control efficiency and the control accuracy of the slag discharge process or the discharge process are improved, and the accuracy and the reliability of the control method of the reaction furnace are further improved.
In an exemplary embodiment of the present disclosure, a height of a discharged melt layer of a reaction furnace is preset, and if the predicted melt layer height is greater than or equal to the height of the discharged melt layer, it is determined that the predicted melt layer height satisfies a discharge condition. And if the predicted melt layer height is smaller than the discharge melt layer height, judging that the predicted melt layer height does not meet the discharge condition.
Further, the method of controlling the reaction furnace will be explained with reference to the following examples.
The first embodiment is as follows:
in the embodiment of the disclosure, based on the energy balance principle and the material balance principle, the component increment in the molten bath of the reaction furnace, the height of a slag layer in the molten bath and the height of a metal layer in the molten bath are predicted, and the predicted values are corrected, and the method specifically comprises the following steps:
step one, calculating the reaction product amount:
(1) obtaining an energy balance formula: the heat of the reaction furnace comprises input electric energy QEHeat of feeding QMFlue gas loss heat QGAnd furnace body and other stray heat losses QLBased on the energy balance principle, the expression of the energy balance formula is obtained as follows:
QE+QM＝QF+QG+QL (1)
as shown in fig. 9, a reactor furnace structure 900 is provided. In a reaction furnace908, in one aspect, the charge W is measured using a load cell in the hopper 902 MMeasuring the temperature T of the feed material using a thermal resistor in the feed hopper 902MWherein, the feed amount W is obtained by matching the feed bin 902 with the feed valve 904MRear pair of feeding heat QMAnd (4) performing calculation. On the other hand, the actual input electric energy Q is measured by a high-voltage side electric energy meter of the furnace transformer 906ERespectively measuring the flue gas flow L by using a flowmeter arranged at the outlet of a fan of the reaction furnace and a thermocouple arranged in a flue of the reaction furnaceGAnd flue gas temperature TGThen, calculating the flue gas loss heat Q of the reaction furnaceGAnd determining the furnace body and the stray loss heat Q according to the heat transfer modelL. In addition, after the slag in the reaction furnace is discharged to the slag tapping ladle trolley 914 by using the slag tapping launder 910, the slag tapping weight is measured by the weighing sensor on the slag tapping ladle trolley 914
Figure BDA0003018352450000091
After the discharge from the inside of the reaction furnace was discharged to the discharge ladle cart 916 by using the discharge chute 912, the discharge weight W 'was measured by the weighing sensor on the discharge ladle cart 916'MT. Sending signals measured by each sensor to a PLC (Programmable Logic Controller) Controller or a DCS (Distributed Control System) Control System of the reaction furnace, completing operation according to the implementation steps in the PLC Controller or the DCS Control System, and obtaining reaction heat according to the heat conservation theorem, wherein the reaction heat is melting and reaction absorption heat Q F。
(2) Calculating electric energy: measuring the number q of electrical degrees in the running time (in hours) by an electric energy meterEInput electric energy QEThe expression of (a) is as follows:
QE＝qE×3.6×106 (2)
wherein the input electric energy QEThe unit of (d) is Joule (abbreviated as Focus, and denoted by the symbol J).
Wherein the electrical degree qEIn kilowatt-hours (symbol kWh).
(3) Is calculated intoMaterial heat quantity: by measuring the temperature T of the feedMAnd the feed amount WMDetermining the heat quantity Q of the feeding material added into the furnaceMHeat of feed QMThe expression of (a) is as follows:
QM＝CM×(TM-T0)×WM (3)
wherein, CMThe specific heat capacity of the hot material is expressed in J/(kg DEG C.) and TMAnd T0In degrees Celsius (symbols are degrees C.), T0Taking 25 ℃ and feeding WMThe unit of (b) is in kilograms (symbol is kg).
(4) Calculating the heat loss of the flue gas: by means of the measured flue gas temperature T during the operating timeGFlue gas flow LGDetermining heat of smoke loss QGThe expression of (a) is as follows:
QG＝CG×(TG-T0)×LG (4)
wherein, CGThe specific heat capacity of the flue gas and the running time of the flue gas are TG-T0，TGFor end of run time, T0For the start time of operation, in hours, the flue gas temperature TGIn degrees centigrade, flue gas flow LGIn cubic meter per second, the heat of the flue gas loss QGIn joules.
(5) Obtaining loss of furnace body and other stray heat: the furnace body and other stray heat loss Q can be calculated by establishing a heat transfer model and an equation LFurnace body and other stray heat losses QLIs in joules, which is considered a constant process because of the relatively slow change and small effect on the discharge cycle.
(6) Calculating the reaction product amount: heat of reaction QFThe expression of (c) is as follows:
QF＝CF×WF (5)
wherein, CFIs the heat value of the reaction of the material, in J/(kg), determined by metallurgical calculation, WFThe amount of the molten hot material is expressed in kg, and the amount of the molten hot material, i.e., the amount of the reaction product, can be further expressed asStep-derived molten heat quantity WFThe expression of (a) is as follows:
WF＝QF/CF (6)
combining the formula (1) to obtain the reaction heat QFThe expression of (a) is as follows:
QF＝QE+QM-QG-QL (7)
wherein the heat of reaction QFIn units of joules.
Obtaining the amount of molten hot material W in the operating time according to the above equations (1) to (8)FThe expression of (a) is as follows:
WF＝QF/CF＝(QE+QM-QG-QL)/CF
＝(qE×3.6×106+CM×(TM-T0)×WM-CG×(TG-T0)×LG-QL)/CF (8)
step two, calculating the discharge amount and the slag discharge amount:
(1) according to the material balance principle, the total amount of each element is unchanged before and after the reaction, and the following values can be determined according to the composition proportion of slag and metal in the reaction product amount and the material balance calculation:
WSRweight of slag/weight of material, WSRThe weight of slag generated for each kilogram of material melted.
WMTRWeight of metal/weight of material, WMTRThe weight of metal produced per kilogram of material is melted.
The weight of metal generated by melting each kilogram of material is the discharge amount, and the weight of slag generated by melting each kilogram of material is the discharge amount.
(2) Slag quantity W produced during operation timeSThe expression of (c) is as follows:
WS＝(WSR×WF)＝WSR×(qE×3.6×106+CM×(TM-T0)×WM-CG×(TG-T0)×LG-QL)/CF(9)
further, the discharge amount W generated in the operation time is calculatedMTThe expression of (c) is as follows:
WMT＝WMTR×(WS/WSR) (10)
step three, training a neural network model:
in actual production, errors exist in all measurement links or material components fluctuate, so that the prediction result is deviated. In order to solve the problem, a Back-propagation (BP) neural network is introduced to construct a neural network model, and a calculation result is corrected. Will input electric energy QEHeat of feeding QMFlue gas loss heat QGAnd taking the three variables as input, taking the slag charge correction value as output, and establishing a neural network model.
As shown in fig. 10, the heat quantity Q of the fed material isMAs a first input 1002 of the neural network model, the smoke loses heat QGThe second input 1004, which is a neural network model, is the furnace and stray lost heat QLAs a third input value 1006 of the neural network model, the required slag correction value Δ W is used as an output value 1010 of the neural network model, and a hidden layer 1008 of the neural network model is added to establish the neural network model as shown in fig. 10.
The neural network model includes, but is not limited to, a BP neural network model and a convolutional neural network.
Wherein the slag correction value comprises a slag discharge correction value and a discharge correction value.
Constructing and iteratively training the neural network model, and comprising the following steps:
(1) and measuring the actual slag quantity after the reaction furnace finishes primary slag discharge, calculating the ideal slag quantity within a period of operation time, and obtaining a slag correction value sample according to the actual slag quantity and the ideal slag quantity.
(2) And measuring and calculating the accumulated input electric energy, the feed heat and the smoke loss heat within a period of operation time to obtain an input electric energy sample, a feed heat sample and a smoke loss heat sample.
(3) And respectively substituting the input electric energy sample, the feeding heat sample, the smoke loss heat sample and the slag correction value sample into the input and the output of the neural network model to carry out iterative training.
Wherein, the slag discharge treatment of the reaction furnace is also suitable for the discharge treatment.
Step four, correcting the calculated value in real time:
the method comprises the following steps of utilizing a constructed neural network model to correct the slag discharge amount and the discharge amount in real time to obtain a final predicted value, and comprising the following steps:
(1) input electric energy Q in running timeEHeat of feed QMAnd flue gas loss heat QGSubstituting the correction value into a neural network model as input to obtain a slag discharge correction value delta W 1。
(2) Correcting the slag amount generated in the running time to obtain a corrected slag amount value M'SThe expression of (c) is as follows:
M′S＝WS-ΔW1 (11)
(3) correcting the discharge amount generated in the running time to obtain a corrected material amount value M'MTThe expression of (c) is as follows:
M′MT＝WMTR×(M′S/WSR) (12)
step five, calculating the height of a molten layer, wherein the height of the molten layer comprises the height of a slag layer and the height of a metal layer:
(1) setting the sectional area of the hearth of the reaction furnace as A and the corresponding height H of the slag layerSAnd metal layer height HMTThe expression of (a) is as follows:
HS＝M′S/(A×ρS) (13)
HMT＝M′MT/(A×ρMT) (14)
where ρ isSIs the slag density, pMTIs the metal density, slag density ρSAnd metal density ρMTDetermined by metallurgical parameters of the reactor, slag density pSAnd metal density ρMTThe unit of (A) is kilogram per cubic meter, the unit of the furnace hearth sectional area A is square meter, and the height H of a slag layerSAnd metal layer height HMTIs given as a unit of meter and a slag quantity value M'SMaterial quantity value M'MTIn kilograms.
(2) After the slag discharging or discharging of the reaction furnace is finished each time, the height H of the slag layer is increasedSOr the height H of the metal layerMTAnd clearing, and clearing the running time at the same time to start the melt layer height prediction of a new period.
Example two:
in the disclosed embodiment, the process of predicting the melt layer height is as follows:
(1) after slag discharge or material discharge is finished, measuring the actual slag discharge amount or the actual discharge amount to obtain the slag quantity value of the actual slag discharge amount and the calculated slag discharge amount or the material quantity value of the actual discharge amount and the calculated discharge amount, and recording input electric energy, feeding heat and smoke loss heat for iterative training of a neural network model.
(2) And zero clearing the height of the slag layer or the height of the metal layer in the reaction furnace, zero clearing the running time, and starting the prediction of the height of the molten layer in a new period.
(3) And measuring the material feeding temperature, the material feeding amount, the smoke temperature, the smoke flow and the input electric energy in real time.
(4) And calculating the slag discharge amount and the discharge amount in the running time.
(5) And correcting the slag discharge amount and the discharge amount according to the correction result of the neural network model to obtain a slag amount value and a material amount value.
(6) And predicting the height of the slag layer and the height of the metal layer in real time according to the slag quantity value and the material quantity value.
As shown in fig. 11, the control system of the reaction furnace includes a material balance model 1102, an energy balance model 1104, and a neural network model 1106.
In an exemplary embodiment of the present disclosure, the control system of the reaction furnace includes measurement instruments (temperature, flow and load cells) and an optimization controller. The measuring instrument transmits a measured signal value of the process parameter to the optimization controller, the energy balance model 1104 is used for calculating reaction heat in the optimization controller, the reaction product amount corresponding to the reaction heat is calculated according to the material balance model 1102, the slag amount and the discharge amount are calculated according to the slag proportion of the reaction product amount to determine the height of a slag layer and the height of a metal layer, the calculated slag amount and the calculated discharge amount are corrected through the neural network model 1106, and the height of the slag layer and the height of the metal layer are predicted according to the corrected slag amount and the corrected discharge amount, so that the slag discharging process and the discharging process of the reaction furnace are controlled according to the predicted height of the slag layer and the height of the metal layer.
Corresponding to the method embodiment, the disclosure also provides a control device of the reaction furnace, which can be used for executing the method embodiment.
Fig. 12 is a block diagram of a control apparatus of a reaction furnace in an exemplary embodiment of the present disclosure.
Referring to fig. 12, the control apparatus 1200 of the reaction furnace may include:
the determining module 1202 is configured to determine the reaction heat of the material according to the input electric energy, the feeding heat, and the loss heat of the reaction furnace.
The determination module 1202 is further configured to determine an amount of a reaction product in the reaction furnace according to the heat of reaction.
The determination module 1202 is further configured to determine a slag proportion in the reaction product amount to determine the amount of slag and/or the amount of discharged material.
And the control module 1204 is used for controlling the slag discharging process of the reaction furnace according to the slag discharging amount and/or controlling the discharging process of the reaction furnace according to the discharging amount.
Since the functions of the apparatus 1200 have been described in detail in the corresponding method embodiments, the disclosure is not repeated herein.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the functionality and features of two or more of the modules or units described above may be embodied in one module or unit, in accordance with embodiments of the present disclosure. Conversely, the functions and functionalities of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Accordingly, various aspects of the present invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 1300 according to this embodiment of the invention is described below with reference to fig. 13. The electronic device 1300 shown in fig. 13 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in fig. 13, the electronic device 1300 is in the form of a general purpose computing device. The components of the electronic device 1300 may include, but are not limited to: the at least one processing unit 1310, the at least one memory unit 1320, and the bus 1330 connecting the various system components including the memory unit 1320 and the processing unit 1310.
The storage unit stores program codes, and the program codes can be executed by the processing unit 1310, so that the processing unit 1310 executes the steps according to various exemplary embodiments of the present invention described in the above "exemplary method" of the present specification. For example, the processing unit 1310 described above may perform a method as shown in an embodiment of the present disclosure.
The storage 1320 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)13201 and/or a cache memory unit 13202, and may further include a read-only memory unit (ROM) 13203.
Storage unit 1320 may also include a program/utility 13204 having a set (at least one) of program modules 13205, such program modules 13205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 1330 may be any bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 1300 may also communicate with one or more external devices 1400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1300 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 1350. Moreover, the electronic device 1300 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as an internet) via the network adapter 13130. As shown, the network adapter 13130 communicates with the other modules of the electronic device 1300 via a bus 1330. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1300, including but not limited to: microcode, device drivers, redundant processing units, external magnetic disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the invention described in the "exemplary methods" above in this specification, when the program product is run on the terminal device.
The program product for implementing the above method according to an embodiment of the present invention may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this respect, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily appreciated that the processes illustrated in the above figures are not intended to indicate or limit the temporal order of the processes. In addition, it is also readily understood that these processes may be performed, for example, synchronously or asynchronously in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (11)

1. A method for controlling a reaction furnace, comprising:
determining the reaction heat of the materials according to the input electric energy, the feeding heat and the loss heat of the reaction furnace;
determining the amount of reaction products in the reaction furnace according to the reaction heat;
determining the slag proportion in the reaction product amount to determine the slag amount and/or the discharge amount;
Controlling the slag discharge process of the reaction furnace according to the slag discharge quantity, and/or controlling the discharge process of the reaction furnace according to the discharge quantity.
2. The method for controlling the reaction furnace according to claim 1, wherein the determining of the reaction heat of the material according to the input electric energy, the input heat and the loss heat of the reaction furnace comprises:
determining the input electric energy of the reaction furnace;
and/or determining the feeding heat according to the feeding amount, the feeding temperature and the heat parameters of the materials of the reaction furnace;
and/or, determining the lost heat according to the structure of the reaction furnace;
and determining the reaction heat of the material according to the input electric energy of the reaction furnace, the feeding heat of the reaction furnace and the loss heat.
3. The method of claim 1, wherein determining the amount of reaction products in the reaction furnace based on the heat of reaction comprises:
determining reaction parameters of reaction products in the reaction furnace;
and determining the reaction product amount according to the reaction heat and the reaction parameter.
4. The method of controlling a reactor according to claim 1, further comprising:
determining an input electric energy sample and a feed heat sample for training a neural network model;
Determining a lost heat sample of the reaction furnace;
determining a slag quantity sample and/or a discharge quantity sample according to the discharge history of the reaction furnace;
and taking the feeding heat sample, the input electric energy sample and the loss heat sample as input samples of the neural network model, and taking the slag output sample and/or the discharge sample as output results of the neural network model, and training the neural network model.
5. The method of claim 4, wherein the neural network model comprises a first sub-model, and the training of the neural network model with the input heat sample, the input power sample, and the loss heat sample as input samples of the neural network model and the output sample as output results of the neural network model comprises:
binding the feed heat sample and the input electrical energy sample into a first sample;
and taking the first sample and the lost heat sample as input samples of the first submodel, and taking the slag output sample and/or the discharge sample as output results of the first submodel to train the neural network model.
6. The method of claim 4, wherein the neural network model comprises a second sub-model, and the training of the neural network model with the input heat sample, the input power sample, and the lost heat sample as input samples of the neural network model and the output sample as output results of the neural network model further comprises:
binding the feed heat sample and the loss heat sample into a second sample;
and taking the second sample and the input electric energy sample as input samples of the second submodel, and taking the slag output sample and/or the discharge sample as output results of the second submodel to train the neural network model.
7. The method of claim 4, wherein the neural network model comprises a third sub-model, and the training of the neural network model with the input heat sample, the input power sample, and the lost heat sample as input samples of the neural network model and the output sample as output results of the neural network model further comprises:
Binding the lost heat sample and the input electrical energy sample into a third sample;
and taking the third sample and the feeding heat sample as input samples of the third submodel, and taking the slag output sample and/or the discharging sample as output results of the third submodel, and training the neural network model.
8. The method of controlling the reaction furnace according to any one of claims 1 to 7, wherein controlling a slag discharge process of the reaction furnace according to the slag discharge amount and/or controlling a discharge process of the reaction furnace according to the discharge amount comprises:
determining the height of a predicted molten layer according to the sectional area of a hearth of the reaction furnace, the slag density and the slag amount;
and/or determining the predicted molten layer height according to the hearth sectional area, the discharging density and the discharging amount of the reaction furnace;
and if the predicted molten layer height meets the discharge condition, discharging the discharged materials and/or discharged slag in the reaction furnace.
9. A control apparatus of a reaction furnace, comprising:
the determining module is used for determining the reaction heat of the material according to the input electric energy, the feeding heat and the loss heat of the reaction furnace;
The determining module is further used for determining the reaction product amount in the reaction furnace according to the reaction heat;
the determining module is further used for determining the slag proportion in the reaction product amount so as to determine the slag amount and/or the discharge amount;
and the control module is used for controlling the slag discharge process of the reaction furnace according to the slag discharge quantity and/or controlling the discharge process of the reaction furnace according to the discharge quantity.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of controlling a reaction furnace according to any one of claims 1 to 8.
11. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of controlling the reaction furnace of any one of claims 1-8 via execution of the executable instructions.
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