要对厌氧消化过程进行控制,首先必须对该过程进行数学建模。数学建模是学习这个该过程的一个有吸引力的工具。Angel idaki, Ellegaard, and Ahring (1999)提出一个包括16个变量与六个主要阶段的模型。Simeonov (1999)研究开发了一个基于实现控制目的厌氧消化二阶非线性模型[6]。65296
但是尽管有长期的实践经验和几十年的学术研究,,厌氧消化的控制仍然是一个待解决的问题,主要原因是,在这个过程中,连续搅拌生物反应釜非线性常微分方程的描述模型中有大量难以进行估计的系数以及有限可在线测量的过程变量。论文网
对一个有限信息被控系统而言,不同的估计如以神经网络模型为基础的方法或者Takagi-Sugeno的模糊观察测都被集成到控制算法中用于系统参数或状态估计,如参数线性化自适应控制、进给速度反馈镇定方法、或非线性PI设定值调节控制等。已经发展到可以控制这个复杂而强非线性过程[4]。
本文所重点研究的针对搅拌生物反应釜甲烷发酵过程中轨迹跟踪复合自适应控制器(CAC)计划被用于实现和控制一个实时的厌氧消化过程。
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