| 基于ANFIS模型的Pr/Nd萃取过程预测控制 |
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| 引用本文： | 杨辉,朱凡,陆荣秀,张志勇. 基于ANFIS模型的Pr/Nd萃取过程预测控制[J]. 化工学报, 2016, 67(3): 982-990. DOI: 10.11949/j.issn.0438-1157.20151978 |
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| 作者姓名： | 杨辉 朱凡 陆荣秀 张志勇 |
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| 作者单位： | 华东交通大学电气与电子工程学院, 江西 南昌 330013;江西省先进控制与优化重点实验室, 江西 南昌 330013 |
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| 基金项目： | 国家自然科学基金项目(51174091,61364013,61563015);国家重点基础研究发展计划前期研究专项(2014CB360502)。 |
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| 摘 要： | 针对稀土萃取过程自动化程度低、经验控制误差大、手动调节效率不高的问题,建立了萃取过程ANFIS模型,实现了各控制流量的自动调节。考虑稀土萃取过程非线性和动态特性,采用自适应神经模糊推理系统(ANFIS)对Pr/Nd萃取过程进行描述,在保证高精度的组分含量预测输出基础上,运用广义预测控制方法(GPC)实现各控制流量的优化控制;最后,基于Pr/Nd萃取过程动态数据进行仿真实验。通过与传统PID方法的实验对比,表明了本文方法的有效性。
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| 关 键 词： | 萃取 非线性 ANFIS 模型 预测控制 |
| 收稿时间： | 2015-12-28 |
| 修稿时间： | 2016-01-08 |
ANFIS model-based predictive control for Pr/Nd cascade extraction process |
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| YANG Hui,ZHU Fan,LU Rongxiu,ZHANG Zhiyong. ANFIS model-based predictive control for Pr/Nd cascade extraction process[J]. Journal of Chemical Industry and Engineering(China), 2016, 67(3): 982-990. DOI: 10.11949/j.issn.0438-1157.20151978 |
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| Authors: | YANG Hui ZHU Fan LU Rongxiu ZHANG Zhiyong |
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| Affiliation: | School of Electrical and Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nangchang 330013, Jiangxi, China |
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| Abstract: | Rare earth (RE) is a national major strategic resource, but there are some problems existed in the RE cascade extraction industry, such as poor levels of automation, large control error and low efficiency of manual adjustment. In this paper a non-linear generalized predictive control (GPC) method based on adaptive neural fuzzy inference system (ANFIS) is proposed to counter these problems. First, in consideration of the nonlinearity and dynamic characteristic of the extraction process, the ANFIS algorithm is employed to describe the process. Then, on the premise of high-precision of component content prediction, the GPC method is exploited to adjust the flows accurately and automatically. Finally, simulation experiments are carried out based on the dynamic data of Pr/Nd cascade extraction process. By the contrast with the conventional PID method, it is validated that the proposed approach is effective. |
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| Keywords: | extraction non-linear ANFIS model predictive control |
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