Hybrid intelligent optimal control for alumina evaporation processes

doi:10.3969/j.issn.0438-1157.2013.12.011

CIESC Journal ›› 2013, Vol. 64 ›› Issue (12): 4342-4347.DOI: 10.3969/j.issn.0438-1157.2013.12.011

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Hybrid intelligent optimal control for alumina evaporation processes

WANG Yonggang¹, PANG Xinfu¹, LI Haibo², CHAI Tianyou²

1. College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, Liaoning, China;
2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, Liaoning, China

Received:2013-08-20 Revised:2013-09-04 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Natural Science Foundation of China (61004009) and the National Key Technology R&D Program of China (2012BAJ26B01).

氧化铝蒸发过程的混合智能优化设定控制

王永刚¹, 庞新富¹, 李海波², 柴天佑²

1. 沈阳农业大学信息与电气工程学院, 辽宁沈阳 110866;
2. 东北大学流程工业国家重点实验室, 辽宁沈阳 110004

通讯作者: 王永刚
作者简介:王永刚（1978- ），男，博士，讲师。
基金资助:
国家自然科学基金项目（61004009）；国家科技支撑计划项目（2012BAJ26B01）。

Abstract

Abstract: The key technical index,namely,alkaline solution concentration of alumina evaporation process,can not be measured on-line.Moreover,it is usually difficult to describe the dynamics characteristic between the techniques indices and the control loops by using an accurate mathematical model.Thus,the existing optimal methods can not solve the optimal control for operation of the alumina evaporation process.In this paper,a hybrid intelligent optimal control method is developed.The proposed method is comprised of a presetting model based on case-base reasoning (CBR),soft sensor model based on recursive partial least squares (RPLS),feedforward and feedback compensators based on expert rules.Simulation is researched by the actual data of the alumina evaporation process.The simulation results show that the proposed methods can control the alkaline solution concentration within their scopes.

Key words: alumina evaporation process, optimal setting, CBR, soft sensor, RPLS

摘要： 氧化铝蒸发过程的关键工艺指标碱液浓度不能在线检测且与控制回路输出之间的动态特性难以用精确的数学模型描述，采用已有的优化控制方法不能实现上述运行层的优化。针对上述问题提出了由基于案例推理的预设定模型，基于专家规则的前馈、反馈补偿模型以及基于块式偏最小二乘的软测量模型组成的混合智能优化设定控制方法。采用某氧化铝厂蒸发过程的实际数据进行仿真实验，实验研究表明该方法可以有效地将碱液浓度控制在工艺要求的区间内。

关键词: 氧化铝蒸发过程, 优化设定, 案例推理, 软测量, 偏最小二乘

CLC Number:

WANG Yonggang, PANG Xinfu, LI Haibo, CHAI Tianyou. Hybrid intelligent optimal control for alumina evaporation processes[J]. CIESC Journal, 2013, 64(12): 4342-4347.

王永刚, 庞新富, 李海波, 柴天佑. 氧化铝蒸发过程的混合智能优化设定控制[J]. 化工学报, 2013, 64(12): 4342-4347.

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Hybrid intelligent optimal control for alumina evaporation processes

氧化铝蒸发过程的混合智能优化设定控制

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