CN101598927A - A neural network-based soda ash carbonization process control system and its control method - Google Patents
A neural network-based soda ash carbonization process control system and its control method Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 76
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000003763 carbonization Methods 0.000 title claims abstract description 31
- 229910000029 sodium carbonate Inorganic materials 0.000 title claims abstract description 18
- 235000017550 sodium carbonate Nutrition 0.000 title claims abstract description 18
- 238000004886 process control Methods 0.000 title claims abstract 8
- 239000002245 particle Substances 0.000 claims abstract description 28
- 238000004088 simulation Methods 0.000 claims description 7
- 238000013178 mathematical model Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000013480 data collection Methods 0.000 claims 1
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- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 abstract description 4
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
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Abstract
本发明涉及一种基于神经网络的纯碱碳化工艺控制系统及其控制方法，包括神经网络控制器、神经网络辨识器、模型库、微粒群优化器、数据采集通道和碳化塔；数据采集通道实时采集碳化塔的工艺参数值，进行数据预处理，将处理后的数据传递给神经网络辨识器，神经网络辨识器进行建模，建模后的模型经过仿真修正后，神经网络控制器即可读取修正后的模型参数，生成控制参数，同时微粒群优化器对控制量进行寻优，得到一组最优控制量后，控制执行机构动作，完成对碳化塔的建模与优化控制。模型库用于存储神经网络辨识器建立的纯碱碳化塔内流量与温度模型。本发明可以对碳化塔进行建模与优化控制，提高单元的自动化水平，增加生产过程钠转换率，降低消耗。
The invention relates to a neural network-based soda ash carbonization process control system and its control method, including a neural network controller, a neural network identifier, a model library, a particle swarm optimizer, a data acquisition channel and a carbonization tower; the data acquisition channel collects in real time The process parameter value of the carbonization tower is preprocessed, and the processed data is passed to the neural network identifier. The neural network identifier performs modeling. After the model is simulated and corrected, the neural network controller can read it. The corrected model parameters are used to generate control parameters. At the same time, the particle swarm optimizer optimizes the control quantities. After obtaining a set of optimal control quantities, the action of the actuator is controlled to complete the modeling and optimization control of the carbonization tower. The model library is used to store the flow and temperature models in the soda ash carbonization tower established by the neural network identifier. The invention can carry out modeling and optimization control on the carbonization tower, improve the automation level of the unit, increase the sodium conversion rate in the production process, and reduce consumption.
Description
Technical field
The present invention relates to soda industry technological process and automation field, particularly a kind of soda carbonization technique control system and control method thereof based on neural network.
Background technology
Soda ash (Na2CO3) is a kind of important basic chemical raw materials, also be a kind of traditional chemical products of producing for many years and using history that have, it is widely used in all conglomeraties such as chemical industry, medicine, glass, metallurgy and papermaking, annual is in great demand, occupy important fundamental position in the development of the national economy, the Development of soda ash industry quality is directly connected to the raising of development and national economy and living standards of the people.
Soda Carbonization Process is the key link of alkali-making process, and mechanism is comparatively complicated.Carbonators is the core cell that whole soda ash is produced, various procedures such as existing chemical reaction, heat transfer, mass transfer, crystallization have the existence of gas, liquid, solid three-phase material again, and every tower is divided into cleaning and system alkali two states again, be existing continuous, complex process is intermittently arranged again.The quality that its operations index is finished is directly connected to product yield and quality, thereby influences the cost and the economic benefit of product.Therefore improve the controlling performance of each key process parameter in the tower, improve the quality of product and the important topic that output is Sodium Carbonate Plant.At present, though domestic each alkali factory carbonization process has adopted computer control system mostly, as DCS and PLC etc., but also rest on the hand/automatic control level of conventional instrument basically, still adopt single-circuit hand/control automatically for the reference mark, controlling performance depends on continued operation level and operator's artificial experience, control accuracy is comparatively coarse, make that the control accuracy of system is not high, and because the frequent artificial interference of operator makes that the fluctuation of system is bigger, thereby be difficult to guarantee the quality of full production and product, utilization ratio of raw materials, the utilization factor of sodium, the conversion ratios of carbon etc. are generally not high.
Therefore, to the modeling and optimization of the key process parameter of Soda Carbonization Process, be in order can be that the production of carbonators is more steady, fluctuation is few, reduce equipment loss, reduce working strength of workers, increase the output of soda ash, its final goal is the utilization factor for the conversion ratio that can improve carbon, sodium, improve the output and the quality of soda ash, improve the utilization factor of load, reduce cost, reduce energy consumption, thereby increase economic benefit.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, design a kind of can be to the modeling and optimization of the key process parameter of Soda Carbonization Process, and reduce cost, reduce energy consumption, thereby increase the soda carbonization technique control system based on neural network of economic benefit, the present invention also comprises the control method of this system.
In order to realize above-mentioned technical purpose, the present invention includes following technical characterictic: a kind of soda carbonization technique control system based on neural network comprises nerve network controller, neural network identifier, model bank, particle swarm optimization device, data acquisition channel and carbonators;
The output terminal of described carbonators is connected with the neural network identifier input end by data acquisition channel; The neural network identifier output terminal is connected with the input end of nerve network controller, and the neural network identifier output terminal is connected with the input end of nerve network controller by model bank; The nerve network controller output terminal is connected with particle swarm optimization device input end, and the nerve network controller output terminal is connected with particle swarm optimization device input end by data acquisition channel; The output terminal of particle swarm optimization device is connected with the input end of carbonators.
Further, in order to realize the emulation correction to modeling process, described neural network identifier comprises neural net model establishing module, model emulation module, model editing module; The output terminal of data acquisition channel is connected with the input end of neural net model establishing module; Data acquisition channel is connected with the model emulation module; Model emulation module output terminal is connected with the neural net model establishing module; Neural net model establishing module output terminal is connected with the input end of model editing module and the input end of model bank.
Further, described data acquisition channel comprises acquisition module, the data preprocessing module that connects successively.
The invention still further relates to a kind of control method of the soda carbonization technique control system based on neural network, comprise the steps: that a, data acquisition channel gather the process parameter value of carbonators in real time, carry out the data pre-service;
Data transfer after b, the processing is given neural network identifier, carries out modeling by neural network identifier, and the model after the modeling is through the emulation correction;
C, nerve network controller read model parameter, generate controlled variable, and the particle swarm optimization device carries out optimizing to controlled quentity controlled variable, obtain one group of optimum control amount after, the control executing mechanism action.
Further, described step b comprises the steps:
The neural net model establishing of b1, neural network identifier is set up mathematical model according to the data that obtain; The model that the data that the utilization of model emulation device obtains are set up neural net model establishing carries out validation verification;
The nonlinear model that b2, model editing module are set up the neural net model establishing module according to simulation result is revised;
B3, neural net model establishing module are gone into model data store in the model bank.
Further, described step c comprises the mode of working online and the mode that works offline, when working online mode, the model parameter that nerve network controller is set up in real time according to neural network identifier generates controlled quentity controlled variable, controlled quentity controlled variable is carried out optimizing at the particle swarm optimization device, the topworks of control tower action during the fructufy of optimization;
When working offline mode, neural network identifier is not worked, nerve network controller generates controlled quentity controlled variable according to the model parameter in the model bank, and the particle swarm optimization device carries out optimizing according to the model of model bank storage to controlled quentity controlled variable, topworks's action of the control tower as a result of optimization.
The present invention compared with prior art, have following beneficial effect: control system of the present invention comprises neural network identifier, nerve network controller and particle swarm optimization device, control theory, Optimum Theory have been made full use of, neural network, System Discrimination, particle swarm optimization, intelligent search algorithm etc., the carbonization technique process is realized detection, control, modeling, optimization, scheduling, management and decision-making, reach the integrated technology that increases output, improves the quality, reduces purposes such as consumption.
Description of drawings
Accompanying drawing 1 is the theory diagram of this modeling and optimization control system;
Accompanying drawing 2 is the neural network identifier structural drawing in the native system.
Embodiment
Principle of work of the present invention is by the analysis to the carbonators reaction mechanism, with the black-box modeling principle, uses the neural net model establishing algorithm, according to the historical data of carbonators operation, sets up the nonlinear model of carbonators feature (temperature and flow).According to the network model of setting up, use the particle swarm optimization algorithm, to the output temperature optimizing, obtain the optimal value of one group of flow, and with the setting value of this optimal value as controller, the control executing mechanism action realizes the optimal control to carbonization process.
Accompanying drawing 1 is the theory diagram of native system, comprises nerve network controller 1, neural network identifier 2, model bank 3, particle swarm optimization device 4, data acquisition channel 5 and carbonators 6; The output terminal of described carbonators 6 is connected with neural network identifier 2 input ends by data acquisition channel 5; Neural network identifier 2 output terminals are connected with the input end of nerve network controller 1, and neural network identifier 2 output terminals are connected with the input end of nerve network controller 1 by model bank 3; Nerve network controller 1 output terminal is connected with particle swarm optimization device 4 input ends, and nerve network controller 1 output terminal is connected with particle swarm optimization device 4 input ends by data acquisition channel 5; The output terminal of particle swarm optimization device 4 is connected with the input end of carbonators 6.
The course of work of system is: data acquisition channel 5 is gathered the process parameter value of carbonators 6 in real time, carry out the data pre-service, comprise and promptly carry out filtering and normalized, give neural network identifier 2 with the data transfer after handling, neural network identifier 2 carries out modeling, after the model process emulation correction after the modeling, nerve network controller 1 promptly can read revised model parameter, generate controlled variable, 4 pairs of controlled quentity controlled variables of particle swarm optimization device are carried out optimizing simultaneously, after obtaining one group of optimum control amount, the modeling and optimization control to carbonators is finished in the control executing mechanism action.Model bank 3 storage inside be flow and temperature model in the soda ash carbonators of setting up with neural network identifier 2.
Its principle of work is: neural network identifier 2 is mainly used in foundation, emulation and the correction of nonlinear model.Data acquisition channel 5 obtains stage casing airshed, hypomere airshed in the carbonization technique in real time, goes out the alkali flow, in and the measured data of discharge, tower middle part temperature (12 circle temperature, 17 circle temperature, 23 circle temperature) etc., pick out abnormal data, carry out pre-service again, sample data and test data are divided into groups according to 1: 1 ratio; Based on sample data, the neural net model establishing module 22 utilization neural net model establishing algorithms in the neural network identifier 2 are set up nonlinear mathematical model; Based on test data, the model that utilizes 23 pairs of neural networks of model emulation module to set up carries out emulation; Model editing module 24 utilizes the result of emulation that model is revised.
The present invention has also comprised the control method of this control system, comprises the steps:
The process parameter value that a, data acquisition channel 5 are gathered carbonators 6 is in real time carried out the data pre-service;
Data transfer after b, the processing is given neural network identifier 2, carries out modeling by neural network identifier 2, and the model after the modeling is through the emulation correction;
C, nerve network controller 1 read model parameter, generate controlled variable, and 4 pairs of controlled quentity controlled variables of particle swarm optimization device are carried out optimizing, obtain one group of optimum control amount after, the control executing mechanism action.
Further, described step b comprises the steps:
The neural net model establishing 22 of b1, neural network identifier 2 is set up mathematical model according to the data that obtain; The model that model emulation device 23 utilizes the data of acquisition that neural net model establishing 22 is set up carries out validation verification;
The nonlinear model that b2, model editing module 24 are set up neural net model establishing module 22 according to simulation result is revised;
B3, neural net model establishing module 22 are gone into model data store in the model bank 3.
Further, native system has two kinds of working methods, and promptly step c comprises the mode of working online and the mode that works offline.
When working online mode, the model parameters that nerve network controller 1 is set up in real time according to neural network identifier 2 generate controlled quentity controlled variables, and controlled quentity controlled variable is carried out optimizing at particle swarm optimization device 4, topworks's action of control tower during the fructufy of optimization.The model that should set up in real time is for determining the parameter of controller, simultaneously, obtain actual operation parameters in the production run by collection, utilize the flow process parameter value of particle swarm optimization algorithm optimizing optimum, controlled quentity controlled variable setting value as controller, the control executing mechanism action, the flow technological parameter of dynamic adjustments carbonization process.After each the calculating, only export current controlled quentity controlled variable and impose on real process.To constantly next, recomputate controlled quentity controlled variable according to new measurement data.
When working offline mode, neural network identifier 2 is not worked, nerve network controller 1 generates controlled quentity controlled variable according to the model parameter in the model bank 3, particle swarm optimization device 4 carries out optimizing according to the model of model bank 3 storages to controlled quentity controlled variable, the optimum control amount that optimizing is obtained is as the setting value of ANN (Artificial Neural Network) Control, topworks's action of control carbonators, thus realize offline optimization control.
The modeling and optimization control system of the soda carbonization technique process that native system provides can be carried out modeling and optimization control to the core cell-carbonators of soda ash production, improve the automatization level of unit, the steady production operating mode increases production run sodium conversion ratio, reduces consuming.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102998973A (en) * | 2012-11-28 | 2013-03-27 | 上海交通大学 | A multi-model adaptive controller and control method for nonlinear systems |
| CN103336433A (en) * | 2013-04-25 | 2013-10-02 | 常州大学 | Back stepping based mixed adaptive predication control system and predication control method thereof |
| CN107544286A (en) * | 2017-08-30 | 2018-01-05 | 浙江力太科技有限公司 | A kind of system identifying method in evaporization process |
| CN109520069A (en) * | 2018-09-29 | 2019-03-26 | 珠海格力电器股份有限公司 | Electronic device control method and device, electronic device and storage medium |
| CN112692147A (en) * | 2020-12-07 | 2021-04-23 | 广东石油化工学院 | Intelligent drawing control system and method for rolled differential-thickness plate box-shaped piece |
| CN113435067A (en) * | 2021-08-26 | 2021-09-24 | 阿里云计算有限公司 | Data processing system and method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201449529U (en) * | 2009-05-15 | 2010-05-05 | 广东工业大学 | A control system of soda ash carbonization process based on neural network |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102998973A (en) * | 2012-11-28 | 2013-03-27 | 上海交通大学 | A multi-model adaptive controller and control method for nonlinear systems |
| CN102998973B (en) * | 2012-11-28 | 2016-11-09 | 上海交通大学 | Multi-model adaptive controller and control method of nonlinear system |
| CN103336433A (en) * | 2013-04-25 | 2013-10-02 | 常州大学 | Back stepping based mixed adaptive predication control system and predication control method thereof |
| CN103336433B (en) * | 2013-04-25 | 2016-10-19 | 常州大学 | Hybrid Adaptive Predictive Control System and Its Predictive Control Method Based on Backstepping Method |
| CN107544286A (en) * | 2017-08-30 | 2018-01-05 | 浙江力太科技有限公司 | A kind of system identifying method in evaporization process |
| CN109520069A (en) * | 2018-09-29 | 2019-03-26 | 珠海格力电器股份有限公司 | Electronic device control method and device, electronic device and storage medium |
| CN112692147A (en) * | 2020-12-07 | 2021-04-23 | 广东石油化工学院 | Intelligent drawing control system and method for rolled differential-thickness plate box-shaped piece |
| CN112692147B (en) * | 2020-12-07 | 2022-12-02 | 广东石油化工学院 | Intelligent drawing control system and method for rolled differential-thickness plate box-shaped piece |
| CN113435067A (en) * | 2021-08-26 | 2021-09-24 | 阿里云计算有限公司 | Data processing system and method |
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