CN110004465B - An intelligent control method and system for producing metal aluminum in a multi-chamber electrolytic cell - Google Patents
An intelligent control method and system for producing metal aluminum in a multi-chamber electrolytic cell Download PDFInfo
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- 229910052782 aluminium Inorganic materials 0.000 title claims abstract description 94
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 28
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 10
- 239000002184 metal Substances 0.000 title claims abstract description 10
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 claims abstract description 85
- 238000005868 electrolysis reaction Methods 0.000 claims abstract description 32
- 238000005070 sampling Methods 0.000 claims description 53
- 238000004088 simulation Methods 0.000 claims description 45
- 238000007599 discharging Methods 0.000 claims description 32
- 238000007781 pre-processing Methods 0.000 claims description 21
- 238000004519 manufacturing process Methods 0.000 claims description 18
- 238000004458 analytical method Methods 0.000 claims description 17
- 239000003792 electrolyte Substances 0.000 claims description 14
- 230000005856 abnormality Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 5
- 125000001153 fluoro group Chemical class F* 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 230000003449 preventive effect Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 150000004673 fluoride salts Chemical class 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000009626 Hall-Héroult process Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- AZDRQVAHHNSJOQ-UHFFFAOYSA-N alumane Chemical compound [AlH3] AZDRQVAHHNSJOQ-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
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- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25C—PROCESSES FOR THE ELECTROLYTIC PRODUCTION, RECOVERY OR REFINING OF METALS; APPARATUS THEREFOR
- C25C3/00—Electrolytic production, recovery or refining of metals by electrolysis of melts
- C25C3/06—Electrolytic production, recovery or refining of metals by electrolysis of melts of aluminium
- C25C3/20—Automatic control or regulation of cells
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Abstract
本发明公开了一种多室电解槽生产金属铝的智能控制方法及系统，该方法包括如下步骤：采集多室电解槽中每个电解室的电压、电流，电流包括阳极电流和阴极电流；根据采集的电压、电流生成每个电解室中的氧化铝下料速率控制指令和或阳极糊添加控制指令；氧化铝下料速率控制指令是根据电解室的电流、氧化铝浓度以及氧化铝下料速度控制规则生成，阳极糊添加控制指令是根据每个电解室的电压、电流、氧化铝浓度以及阳极糊添加规则生成，根据氧化铝下料速率控制指令、阳极糊添加控制指令控制每个电解室的氧化铝下料速率、阳极糊添加速率。本发明该方法实现了多室电解槽的智能控制，尤其是在氧化铝下料速率和阳极糊添加速率。
The invention discloses an intelligent control method and system for producing metal aluminum in a multi-chamber electrolytic cell. The method includes the following steps: collecting the voltage and current of each electrolysis cell in the multi-chamber electrolytic cell, and the current includes anode current and cathode current; The collected voltage and current generate the alumina feeding rate control command and or anode paste adding control command in each electrolysis chamber; the alumina feeding rate control command is based on the current, alumina concentration and alumina feeding speed of the electrolysis chamber The control rules are generated, and the anode paste addition control instructions are generated according to the voltage, current, alumina concentration and anode paste addition rules of each electrolysis chamber. Alumina feed rate, anode paste addition rate. The method of the invention realizes the intelligent control of the multi-chamber electrolytic cell, especially the alumina feeding rate and the anode paste addition rate.
Description
Technical Field
The invention belongs to the technical field of aluminum electrolysis cells, and particularly relates to an intelligent control method and system for producing metal aluminum by a multi-chamber electrolytic cell.
Background
The Hall-Heroult process (Hall-Heroult) aluminum electrolysis process is always the only method for industrial aluminum production, and the core reaction is carried out in an aluminum electrolysis cell. The current mainstream electrolytic cells are large or super large prebaked anode aluminum electrolytic cells, although the capacity of the electrolytic cells is 600kA, the electrolytic cells have the defects of low electric energy utilization rate, poor gas collection effect and the like, and multi-chamber aluminum electrolytic cells for continuous electrolysis with super-low polar distance are designed for further deep energy conservation of aluminum electrolysis.
The multi-chamber aluminum electrolysis cell has the advantages of huge energy saving, continuous electrolysis production, long-term stable cell condition, easy maintenance and low cost in the production process, and can realize multi-stage recovery of flue gas and waste heat, and provide important guarantee for deep energy saving and environmental protection of aluminum electrolysis. However, the existing aluminum electrolysis cell control technology is directed at a single-chamber aluminum electrolysis cell, and the control is performed based on a single comprehensive cell resistance signal and an estimation of alumina concentration, but in an actual process fault, the current and voltage of the electrolysis cell have a great influence on the electrolysis cell, so that the existing control method based on a single element cannot meet the actual requirement, and the compatibility of the multi-chamber electrolysis cell is not good. In addition, the process technology management of the existing electrolytic aluminum enterprises is basically based on experience, which also hinders the inheritance of the process technology of the electrolytic cell, and the accuracy and the efficiency are not ensured.
In view of the above, the reliability of the conventional aluminum electrolytic cell control technology is yet to be improved, and no researchers have conducted intensive research on the multi-chamber aluminum electrolytic cell control technology.
Disclosure of Invention
The invention aims to provide an intelligent control method and system for producing metal aluminum by a multi-chamber electrolytic cell, which realize intelligent control on the multi-chamber electrolytic cell, in particular to provide an alumina blanking rate control instruction and an anode paste adding control instruction which are suitable for the multi-chamber electrolytic cell.
On one hand, the invention provides an intelligent control method for producing metal aluminum by a multi-chamber electrolytic cell, which comprises the following steps:
s1: collecting voltage and current of each electrolytic chamber in the multi-chamber electrolytic cell, wherein the current comprises anode current and cathode current;
s2: generating an alumina blanking rate control instruction and/or an anode paste adding control instruction in each electrolytic chamber according to the voltage and the current collected in the step S1;
the alumina feeding rate control instruction is generated according to the current of an electrolytic chamber, the alumina concentration and an alumina feeding rate control rule, and the alumina feeding rate control rule is as follows: the predicted speed of alumina blanking is calculated according to the following regression function:
wherein f (x) is the predicted alumina blanking rate, l1 is the total number of data sampling points in the alumina self-simulation time period, the alumina self-simulation time period is the interval time period from the previous alumina blanking time to the current predicted alumina blanking time or the partial time period of the interval time period, αiThe real-time current of the electrolytic chamber at the ith sampling point in the self-simulation time period of the alumina is the average value of the anode current and the cathode current,
the average current of real-time current of each sampling point in the alumina self-simulation time interval is obtained,
and
respectively the local concentration y of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period samplesiIn order to weight the fitting constant(s),
b is a constant, which is a self-setting high-order function;
the anode paste adding control command is generated according to the voltage, the current, the alumina concentration and the anode paste adding rule of each electrolytic chamber, and the anode paste adding rule is as follows: the predicted anode paste blanking rate was calculated according to the following regression function:
wherein g (x) is the predicted anode paste blanking rate, l2 is the total number of data sampling points in the simulated time period of the anode paste, and the simulated time period of the anode paste is the interval time period from the previous anode paste blanking time to the current predicted anode paste blanking time or the partial time period of the interval time period，βi、viThe real-time current and the real-time voltage of the electrolytic chamber at the ith sampling point in the self-simulation time period of the anode paste are obtained, the real-time current is the average value of the anode current and the cathode current,
the average current of the real-time current of each sampling point in the simulated time interval is taken as the anode paste,
the average voltage of the real-time voltage of each sampling point in the simulated time interval is taken as the anode paste,
and
respectively is the local concentration Y of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period samplesiIs a weighted fitting constant, n is a constant;
s3: and controlling the alumina feeding rate and the anode paste adding rate of each electrolytic chamber according to the alumina feeding rate control instruction and the anode paste adding control instruction.
Further preferably, the weighted fitting constant y in the alumina blanking speed control ruleiIs generated by fitting the following rules:
the weighted fitting constant Y in the anode paste adding ruleiIs generated by fitting the following rules:
further preferably, the step S1 further collects real-time temperature of each electrolytic cell, and before the step S3 is executed, the method further includes: carrying out simulation control on the multi-chamber electrolytic cell according to the control instruction of the step S2, identifying the stable state of the electrolytic cell according to the simulation result, and generating an abnormal prompting or preventing operation instruction of the electrolytic cell if the electrolytic cell is unstable;
wherein, the real-time temperature of the electrolyte is higher than 960 ℃ or lower than 940 ℃, and an instruction for prompting the abnormality of the electrolytic cell is generated; when the current magnitude of the current suddenly increased or decreased in response to the electrolytic cell is within 30 percent, generating an alarm instruction; when the current of the electrolytic cell suddenly increases or decreases and the current magnitude exceeds 30%, an operation instruction of automatic tripping power-off is generated.
Further preferably, the method also comprises the steps of generating an aluminum discharging control instruction in each electrolytic chamber according to the current of the electrolytic chamber, and performing aluminum discharging control according to the aluminum discharging control instruction;
the aluminum discharge control instruction is generated according to the current of the electrolytic chamber and an aluminum discharge control rule, wherein the aluminum discharge control rule is as follows: calculating the aluminum yield of theoretical electrolysis accumulated in a preset time interval, and generating the aluminum amount according to the aluminum yield;
P＝0.3356I·η·t
wherein P is the cumulative theoretical electrolytic aluminum production over time t, I is the average of cathodic currents over time t in units of A, and η is the current efficiency of the electrolysis process in units of%.
Further preferably, the method also comprises the steps of collecting the real-time temperature of each electrolytic chamber, generating a temperature control instruction according to the real-time temperature of each electrolytic chamber and controlling the temperature according to the temperature control instruction;
the temperature control instruction is generated according to the real-time temperature of the electrolytic chamber and a temperature control rule, wherein the temperature control rule is as follows: when the real-time temperature of the electrolytic chamber deviates from a preset industrial design threshold value, the addition amount of the fluorine salt is increased or reduced, so that the molecular ratio in the electrolytic chamber is controlled to be between 2.3 and 2.4.
On the other hand, the invention provides a system based on the method, which comprises a distributed data sampling unit, a tank condition analysis unit and an execution unit which are sequentially connected in a communication manner;
the distributed data sampling unit comprises a sampling element of each electrolytic chamber and is used for collecting data of each electrolytic chamber, and the collected data at least comprises voltage and current of each electrolytic chamber;
the tank condition analysis unit is used for generating control instructions according to the acquired data, and the control instructions at least comprise an alumina blanking rate control instruction and an anode paste adding control instruction;
and the execution unit is used for executing control operation according to the control instruction generated by the tank condition analysis unit.
Further preferably, the sampling element of each electrolytic chamber comprises an electrolyte temperature measuring module and an electric signal sampling module, and the electrolyte temperature measuring module is an industrial grade thermocouple.
Further preferably, the tank condition analysis unit comprises a real-time simulation module and an exception handling module;
the real-time simulation module is used for performing a simulation control test on each electrolytic chamber according to the generated control instruction and outputting a simulation result to identify the stable state of the electrolytic cell;
the abnormality processing module is used for responding to the unstable state of the electrolytic cell to generate an electrolytic cell abnormality prompt or a preventive operation instruction.
Preferably, the cell condition analysis unit comprises a blanking control module, an anode paste control module, a temperature control module and an aluminum discharging control module;
the blanking control module is used for generating an alumina blanking rate control instruction in each electrolytic chamber;
the anode paste control module is used for generating an anode paste adding control instruction in each electrolytic chamber;
the temperature control module is used for generating a temperature control instruction in each electrolytic chamber;
the aluminum discharging control module is used for generating aluminum discharging control instructions in each electrolytic chamber.
Further preferably, the system further comprises a database unit and a data preprocessing unit, wherein the database unit and the data preprocessing unit are in communication connection with the distributed data sampling unit and the tank condition analyzing unit, the database unit is used for storing data, and the data preprocessing unit is used for preprocessing the acquired original data.
Advantageous effects
The invention provides an intelligent control method and system for producing metal aluminum by a multi-chamber electrolytic cell, which realize intelligent control of the multi-chamber electrolytic cell, particularly provides an alumina blanking rate control instruction and an anode paste adding control instruction which are suitable for the multi-chamber electrolytic cell, considers the current and/or voltage of each electrolytic cell, obtains an alumina blanking rate control rule and an anode paste adding rule through research, realizes reliable calculation of the rate, and solves the defects caused by empirical management. In particular, the anode paste adding rule is set by the invention aiming at the self-baking anode mode of the multi-chamber aluminum electrolytic cell, and no relevant records are provided in the prior art. The invention can simultaneously automate the operation management of the production process of the multi-chamber aluminum electrolytic cell, thereby freeing the operators on site and realizing the omnibearing digital control of the abnormal conditions in the production process of the multi-chamber aluminum electrolytic cell.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent control system for a multi-chamber aluminum electrolysis cell provided by the invention;
FIG. 2 is a schematic structural diagram of an intelligent control system for a multi-chamber aluminum reduction cell according to an embodiment of the present invention, wherein reference numeral 70 denotes the multi-chamber aluminum reduction cell;
fig. 3 is a schematic structural diagram of a distributed data sampling unit in an intelligent control system of a multi-chamber aluminum electrolysis cell provided by the embodiment of the invention.
Detailed Description
The present invention will be further described with reference to the following examples.
As shown in fig. 1, the intelligent control system for multi-chamber electrolytic cell production of metallic aluminum according to the embodiment of the present invention includes a distributed data sampling unit 10, a data preprocessing unit 20, a cell condition analyzing unit 30, an executing unit 40 and a database unit 50. The distributed data sampling unit 10 is connected with the data preprocessing unit 20 and the database unit 50, the tank condition analyzing unit 30 is connected with the data preprocessing unit 20, the database unit 50 and the executing unit 40 in a communication manner, and the data preprocessing unit 20 is connected with the database unit 50 in a communication manner. Wherein, the communication connection is established between each unit through a transmission control protocol/internet protocol (TCP/IP).
As shown in fig. 2, the distributed data sampling unit 10 includes a sampling element for each electrolytic chamber for collecting data of each electrolytic chamber, the collected data including voltage, current, and real-time temperature of each electrolytic chamber. The sampling element comprises an electrolyte temperature measuring module 11 and an electric signal sampling module 12, wherein the electrolyte temperature measuring module 11 is used for measuring the temperature of the electrolyte in the electrolytic cell in real time. The electrical signal sampling module 12 is used for collecting voltage and current data in the electrolytic cell, and it adopts the conventional voltage and current sampling devices in the prior art to collect data, and each electrolytic chamber is provided with a set of voltage and current sampling devices, wherein the current sampling needs to be accessed into the sampling devices from the anode port and the cathode port, and output as anode current and cathode current signals respectively.
The data preprocessing unit 20 is configured to perform preprocessing on the acquired raw data, which includes filtering to remove noise and/or abnormal data rejection. The data preprocessing unit 20 includes a data preprocessing module 21 and a display module 22, where the data preprocessing module 21 is used for preprocessing data, and the display module 22 is used for displaying data information in the system.
The cell condition analyzing unit 30 is used for generating a control instruction (implemented by a programmable logic controller PLC), and the cell condition analyzing unit 30 of the present embodiment includes a blanking control module 31, an aluminum discharging control module 32, an anode paste control module 33, a temperature control module 34, a real-time simulation module 35, and an abnormality processing module 36. The blanking control module 31 is used for generating an alumina blanking rate control instruction in each electrolytic chamber; the aluminum discharging control module 32 is used for generating an aluminum discharging control command in each electrolytic chamber. The anode paste control module 33 is used to generate an anode paste addition control instruction in each of the electrolysis chambers. The temperature control module 34 is used to generate temperature control commands in each of the electrolysis chambers. The real-time simulation module 35 is used for performing a simulation control test on each electrolytic cell according to the generated control instruction, outputting a simulation result to identify the stable state of the electrolytic cell, automatically building a virtual multi-chamber aluminum electrolytic cell by combining a built-in industrial model, performing dynamic real-time simulation, and judging the stable condition of each electrolytic cell. The abnormality processing module 36 is used for generating an electrolytic bath abnormality prompt or preventive operation instruction in response to an electrolytic bath unstable state.
The execution unit 40 comprises a hopper execution module 41, a fluorine salt adding module 42, an anode paste adding module 43, an aluminum discharging module 44 and an emergency tripping module 45, wherein the hopper execution module 41 is used for adjusting the aluminum oxide discharging rate according to the discharging rate control instruction generated by the tank condition analysis unit and executing the aluminum oxide discharging operation. The fluoride salt adding module 42 is configured to generate a fluoride salt adding instruction in a matching manner according to the alumina blanking rate control instruction and the temperature control instruction generated by the bath condition analyzing unit and by combining with a factory actually set fluoride salt adding standard, and execute fluoride salt adding operation. The anode paste adding module 43 is used for executing an anode paste adding operation through a closed anode paste automatic blanking pipeline according to the anode paste adding control instruction generated by the tank condition analysis unit. The aluminum discharging module 44 is configured to discharge aluminum according to the aluminum discharging control command generated by the tank condition analyzing unit, wherein a factory can perform aluminum discharging in a self-fitting proper ratio according to the theoretical aluminum discharge amount P, and perform an aluminum discharging operation, such as a vacuum ladle operation. And the emergency trip module 45 is used for executing exception handling operation according to the exception handling control instruction generated by the tank condition analysis unit, wherein the exception handling operation comprises prompting manual intervention, tripping power failure and the like.
In the embodiment of the invention, the temperature of the electrolyte in each electrolytic cell of the electrolytic cell and the voltage and current data in the electrolytic cell are measured in real time by the electrolyte temperature measuring module 11 and the electric signal sampling module 12 in the distributed data sampling unit 10 of the multi-chamber aluminum electrolytic cell, as shown in fig. 3. The data preprocessing module 21 in the data preprocessing unit 20 filters and denoises the original data acquired by the aluminum cell information acquisition unit and/or rejects abnormal data, so as to obtain effective data, and the display module 22 is used for displaying various data information in the system. A blanking control module 31 in the cell condition analysis unit 30 generates an alumina blanking rate control instruction according to the current of the electrolytic cell, the alumina concentration and the alumina blanking speed control rule; the aluminum discharging control module 32 is used for generating an aluminum discharging control instruction according to the pretreated effective data of the current of the electrolytic cell and an aluminum discharging amount control rule; the anode paste control module 33 generates an anode paste adding control instruction according to the pretreated voltage, current and alumina concentration of the electrolytic cell and the anode paste adding rule; the temperature control module 34 is used for generating a temperature control instruction according to the preprocessed real-time temperature data of the electrolyte in the electrolytic cell and the temperature control rule; the real-time simulation module 35 performs dynamic simulation prediction based on the actual three-dimensional model of the industrial multi-chamber aluminum electrolysis cell placed inside in advance according to the data set of the local cell voltage, current and alumina concentration of each chamber transmitted by the data preprocessing unit 20. When the tank conditions are stable and the process parameters meet the production specifications, the real-time simulation module 35 does not make a feedback. When the simulation result indicates that a local tank condition is about to change, the real-time simulation module 35 calls the relevant optimization rule in the exception handling module 36 to prevent in advance. The exception handling module 36 is configured to identify the optimized instruction processed by the real-time simulation module 35, and generate an exception handling control instruction according to an exception handling rule. Wherein the exception handling operation comprises prompting manual intervention and tripping power failure. Instructions for generating an indication of an abnormal condition of the cell, such as in response to a real-time temperature of the electrolyte being greater than 960 ℃ or less than 940 ℃; when the current magnitude of the current suddenly increased or decreased in response to the electrolytic cell is within 30 percent, generating an alarm instruction; when the current of the electrolytic cell suddenly increases or decreases and the current magnitude exceeds 30%, an operation instruction of automatic tripping power-off is generated.
The hopper execution module 41 in the execution unit 40 is configured to adjust the blanking rate according to the blanking rate control instruction generated by the tank condition analysis unit, and execute the blanking operation; the fluoride salt adding module 42 generates a fluoride salt adding instruction in a matching manner according to the blanking rate control instruction and the temperature control instruction generated by the tank condition analyzing unit 30 and the fluoride salt adding standard actually set by the factory, and executes the fluoride salt adding operation. The anode paste adding module 43 executes anode paste adding operation through a closed anode paste automatic blanking pipeline according to the anode paste adding control instruction generated by the tank condition analysis unit; the aluminum discharging module 44 is configured to execute an aluminum discharging operation according to the aluminum discharging control instruction generated by the tank condition analyzing unit; the emergency trip module 45 is used for executing exception handling operation according to the exception handling control instruction generated by the tank condition analysis unit, wherein the exception handling operation comprises prompting manual intervention and tripping power failure.
The background data management module 51 in the database unit 50 stores the valid data processed in the data preprocessing unit 20, the instructions generated by the tank condition analyzing unit 30, and the operation records executed in the execution unit 40, so as to facilitate the recording and maintenance of the whole production process.
It should be noted that the above-mentioned embodiment is an optimized embodiment, the control system of the embodiment has functions of blanking control, anode paste control, aluminum discharge control and temperature control, and other feasible embodiments may have a plurality of or part of the functions thereof.
The embodiment of the invention provides an intelligent control method for producing metal aluminum by a multi-chamber electrolytic cell, which comprises the following steps:
s1: collecting the voltage, the current and the real-time electrolyte temperature of each electrolytic chamber in the multi-chamber electrolytic cell, wherein the current comprises anode current and cathode current, and the voltage is the pressure difference between the two ends of the anode and the cathode;
s2: generating an alumina blanking rate control instruction, an anode paste adding control instruction, an aluminum discharging control instruction and a temperature control instruction in each electrolytic chamber according to the voltage, the current and the temperature collected in the step S1;
the alumina feeding rate control instruction is generated according to the current of an electrolytic chamber, the alumina concentration and an alumina feeding rate control rule, and the alumina feeding rate control rule is as follows: the predicted speed of alumina blanking is calculated according to the following regression function:
wherein f (x) is the predicted alumina blanking rate, l1 is the total number of data sampling points in the alumina self-simulation time period, the alumina self-simulation time period is the interval time period from the previous alumina blanking time to the current predicted alumina blanking time or the partial time period of the interval time period, αiThe real-time current of the electrolytic chamber at the ith sampling point in the self-simulation time period of the alumina is the average value of the anode current and the cathode current,
the average current of real-time current of each sampling point in the alumina self-simulation time interval is obtained,
and
respectively the local concentration (smaller space, two arbitrary points around) of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period is sampled, yiIn order to weight the fitting constant(s),
b is a constant, which is a self-setting high-order function;
wherein the weighted fitting constant y in the alumina blanking speed control ruleiIs generated by fitting the following rules:
the anode paste adding control command is generated according to the voltage, the current, the alumina concentration and the anode paste adding rule of each electrolytic chamber, and the anode paste adding rule is as follows: the predicted anode paste blanking rate was calculated according to the following regression function:
wherein g (x) is predictedThe blanking rate of the anode paste, l2, is the total number of data sampling points in an anode paste self-simulation time period, the anode paste self-simulation time period is an interval time period or a partial time period of the interval time period from the previous blanking time of the anode paste to the current predicted blanking time of the anode paste, βi、viThe real-time current and the real-time voltage of the electrolytic chamber at the ith sampling point in the self-simulation time period of the anode paste are obtained, the real-time current is the average value of the anode current and the cathode current,
the average current of the real-time current of each sampling point in the simulated time interval is taken as the anode paste,
the average voltage of the real-time voltage of each sampling point in the simulated time interval is taken as the anode paste,
and
respectively is the local concentration Y of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period samplesiIs a weighted fitting constant, n is a constant;
wherein, the anode paste adding rule is weighted with a fitting constant YiIs generated by fitting the following rules:
the aluminum discharge control instruction is generated according to the current of the electrolytic chamber and an aluminum discharge control rule, wherein the aluminum discharge control rule is as follows: calculating the aluminum yield of theoretical electrolysis accumulated in a preset time interval, and generating the aluminum amount according to the aluminum yield;
P＝0.3356I·η·t
wherein P is the cumulative theoretical electrolytic aluminum production over time t, I is the average of cathodic currents over time t in units of A, and η is the current efficiency of the electrolysis process in units of%.
Wherein, the factory can produce aluminum according to the theoretical aluminum yield P value and self-fitting proper proportion.
The temperature control instruction is generated according to the real-time temperature of the electrolytic chamber and a temperature control rule, wherein the temperature control rule is as follows: when the real-time temperature of the electrolytic chamber deviates from a preset industrial design threshold value, the addition amount of the fluorine salt is increased or reduced, so that the molecular ratio in the electrolytic chamber is controlled to be between 2.3 and 2.4.
S3: and controlling the alumina blanking rate, the anode paste adding rate, the aluminum discharging control and the temperature of each electrolytic chamber according to the alumina blanking rate control instruction, the anode paste adding control instruction, the aluminum discharging control instruction and the temperature control instruction.
In conclusion, the system can liberate field operators, realize the omnibearing digital control of blanking, anode paste control, aluminum discharge and abnormal conditions in the production process, realize the high-efficiency unmanned production of the multi-chamber aluminum electrolysis cell, complete digitalization of the metal aluminum electrolysis production process by the system provided by the invention, form the digital aluminum electrolysis cell and realize the important crossing of the informatization of the production process of the multi-chamber aluminum electrolysis cell.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.
Claims (9)
1. An intelligent control method for producing metal aluminum by a multi-chamber electrolytic tank is characterized in that: the method comprises the following steps:
s1: collecting voltage and current of each electrolytic chamber in the multi-chamber electrolytic cell, wherein the current comprises anode current and cathode current;
s2: generating an alumina blanking rate control instruction and/or an anode paste adding control instruction in each electrolytic chamber according to the voltage and the current collected in the step S1;
the alumina feeding rate control instruction is generated according to the current of an electrolytic chamber, the alumina concentration and an alumina feeding rate control rule, and the alumina feeding rate control rule is as follows: the predicted speed of alumina blanking is calculated according to the following regression function:
wherein f (x) is the predicted alumina blanking rate, l1 is the total number of data sampling points in the alumina self-simulation time period, the alumina self-simulation time period is the interval time period from the previous alumina blanking time to the current predicted alumina blanking time or the partial time period of the interval time period, αiThe real-time current of the electrolytic chamber at the ith sampling point in the self-simulation time period of the alumina is the average value of the anode current and the cathode current,
the average current of real-time current of each sampling point in the alumina self-simulation time interval is obtained,
and
respectively the local concentration y of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period samplesiIn order to weight the fitting constant(s),
b is a constant, which is a self-setting high-order function;
the anode paste adding control command is generated according to the voltage, the current, the alumina concentration and the anode paste adding rule of each electrolytic chamber, and the anode paste adding rule is as follows: the predicted anode paste blanking rate was calculated according to the following regression function:
wherein g (x) is the predicted anode paste blanking rate, l2 is the total number of data sampling points in the simulated time period of the anode paste, the simulated time period is the interval time period from the previous anode paste blanking time to the current predicted anode paste blanking time or the partial time period of the interval time period, βi、viThe real-time current and the real-time voltage of the electrolytic chamber at the ith sampling point in the self-simulation time period of the anode paste are obtained, the real-time current is the average value of the anode current and the cathode current,
the average current of the real-time current of each sampling point in the simulated time interval is taken as the anode paste,
the average voltage of the real-time voltage of each sampling point in the simulated time interval is taken as the anode paste,
and
respectively is the local concentration Y of the alumina at two positions at the current collection point side when the ith sampling point in the alumina self-simulation time period samplesiIs a weighted fitting constant, n is a constant;
s3: correspondingly controlling the alumina blanking rate and the anode paste adding rate of each electrolytic chamber according to the alumina blanking rate control instruction and the anode paste adding control instruction;
weighting fitting constant y in alumina blanking speed control ruleiIs obtained by fitting the following rulesThe composition is as follows:
the weighted fitting constant Y in the anode paste adding ruleiIs generated by fitting the following rules:
2. the method of claim 1, wherein: the step S1 further includes, before the step S3 is executed, acquiring a real-time temperature of each electrolytic cell: carrying out simulation control on the multi-chamber electrolytic cell according to the control instruction of the step S2, identifying the stable state of the electrolytic cell according to the simulation result, and generating an abnormal prompting or preventing operation instruction of the electrolytic cell if the electrolytic cell is unstable;
wherein, the real-time temperature of the electrolyte is higher than 960 ℃ or lower than 940 ℃, and an instruction for prompting the abnormality of the electrolytic cell is generated; when the current magnitude of the current suddenly increased or decreased in response to the electrolytic cell is within 30 percent, generating an alarm instruction; when the current of the electrolytic cell suddenly increases or decreases and the current magnitude exceeds 30%, an operation instruction of automatic tripping power-off is generated.
3. The method of claim 1, wherein: generating an aluminum discharging control instruction in each electrolytic chamber according to the voltage and the current acquired in the step S1, and performing aluminum discharging control according to the aluminum discharging control instruction;
the aluminum discharge control instruction is generated according to the current of the electrolytic chamber and an aluminum discharge control rule, wherein the aluminum discharge control rule is as follows: calculating the aluminum yield of theoretical electrolysis accumulated in a preset time interval, and generating the aluminum amount according to the aluminum yield;
P＝0.3356I·η·t
wherein P is the cumulative theoretical electrolytic aluminum production over time t, I is the average of cathodic currents over time t in units of A, and η is the current efficiency of the electrolysis process in units of%.
4. The method of claim 1, wherein: the method also comprises the steps of collecting the real-time temperature of each electrolytic chamber, generating a temperature control instruction according to the real-time temperature of each electrolytic chamber and controlling the temperature according to the temperature control instruction;
the temperature control instruction is generated according to the real-time temperature of the electrolytic chamber and a temperature control rule, wherein the temperature control rule is as follows: when the real-time temperature of the electrolytic chamber deviates from a preset industrial design threshold value, the addition amount of the fluorine salt is increased or reduced, so that the molecular ratio in the electrolytic chamber is controlled to be between 2.3 and 2.4.
5. A system based on the method of any one of claims 1-4, characterized by: the system comprises a distributed data sampling unit, a tank condition analysis unit and an execution unit which are sequentially in communication connection;
the distributed data sampling unit comprises a sampling element of each electrolytic chamber and is used for collecting data of each electrolytic chamber, and the collected data at least comprises voltage and current of each electrolytic chamber;
the tank condition analysis unit is used for generating control instructions according to the acquired data, and the control instructions at least comprise an alumina blanking rate control instruction and an anode paste adding control instruction;
and the execution unit is used for executing control operation according to the control instruction generated by the tank condition analysis unit.
6. The system of claim 5, wherein: the sampling element of each electrolytic chamber comprises an electrolyte temperature measuring module and an electric signal sampling module, and the electrolyte temperature measuring module is an industrial-grade thermocouple.
7. The system of claim 5, wherein: the tank condition analysis unit comprises a real-time simulation module and an exception handling module;
the real-time simulation module is used for performing a simulation control test on each electrolytic chamber according to the generated control instruction and outputting a simulation result to identify the stable state of the electrolytic cell;
the abnormality processing module is used for responding to the unstable state of the electrolytic cell to generate an electrolytic cell abnormality prompt or a preventive operation instruction.
8. The system of claim 5, wherein: the tank condition analysis unit comprises a blanking control module, an anode paste control module, a temperature control module and an aluminum discharging control module;
the blanking control module is used for generating an alumina blanking rate control instruction in each electrolytic chamber;
the anode paste control module is used for generating an anode paste adding control instruction in each electrolytic chamber;
the temperature control module is used for generating a temperature control instruction in each electrolytic chamber;
the aluminum discharging control module is used for generating aluminum discharging control instructions in each electrolytic chamber.
9. The system of claim 5, wherein: the device also comprises a database unit and a data preprocessing unit which are in communication connection with the distributed data sampling unit and the tank condition analyzing unit, wherein the database unit is used for storing data, and the data preprocessing unit is used for preprocessing the acquired original data.
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