基于快速粒子群算法的蒸发过程优化控制

柴琴琴 林琼斌 林双杰

doi:10.12034/j.issn.1009-606X.216286

过程工程学报 >

2017 , Vol. 17 >Issue 3: 539 - 544

DOI: https://doi.org/10.12034/j.issn.1009-606X.216286

过程与工艺

基于快速粒子群算法的蒸发过程优化控制

柴琴琴林琼斌林双杰

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1. 福州大学电气工程与自动化学院，福建福州 350108；2. 福州大学先进控制技术研究中心，福建福州 350108

收稿日期: 2016-08-30

修回日期: 2016-12-12

网络出版日期: 2017-06-14

基金资助

福建省自然科学基金项目

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Optimal Control of an Evaporation Process Using a Fast Particle Swarm Optimization Algorithm

Qinqin CHAI Qiongbin LIN Shuangjie LIN

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1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China;
2. Research Center for Advanced Process Control, Fuzhou University, Fuzhou, Fujian 350108, China

Received date: 2016-08-30

Revised date: 2016-12-12

Online published: 2017-06-14

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摘要

基于欧拉法，改进了蒸发过程离散时滞动态模型，构建了在线非线性预测控制模型. 针对时滞系统小采样周期与长预测控制域带来的计算负担，提出了快速粒子群算法的求解方法，保证控制实时性. 实例模拟表明，在采样时间足够小时仍能保证实时性，液位和浓度很快达到设定范围，新蒸汽消耗量下降1%，可节约蒸汽0.6 t/h.

关键词： 蒸发过程; 优化控制; 时滞; 粒子群算法

本文引用格式

柴琴琴林琼斌林双杰 . 基于快速粒子群算法的蒸发过程优化控制[J]. 过程工程学报, 2017 , 17(3) : 539 -544 . DOI: 10.12034/j.issn.1009-606X.216286

Abstract

Using the improved Euler method, a discrete dynamic model for evaporation process was firstly built. Then a nonlinear prediction control model was constructed. For time-delayed system, the sampling period should be small but the prediction control period should be long. These time requirements resulted in serious computational burden. To improve computational speed, a fast particle swarm algorithm was proposed to ensure the real-time control. And simulation results of a real evaporation process showed that even the sampling time was enough small. In addition, the levels and concentration reach the desired range in a short time, and the live steam consumption was decreased by 1% and saved about 0.6 t/h.

Key words： evaporation process; optimal control; time delay; particle swarm optimization

参考文献

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