Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools
Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools
复制标题
蒸馏塔多回路控制系统的设计：回顾过去和最近的数学工具
DOI：
10.1515/cppm-2020-0070
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发表时间：
2021
影响因子：
0.9
通讯作者：
Ali M. Sahlodin
中科院分区：
文献类型：
--
作者：
Changsoo Kim;Manas Shah;Ali M. Sahlodin
精馏塔控制结构的设计包括选择适当的被操纵和被控制变量（通常包括用于产品成分推理控制的托盘温度）和两组变量之间的一对一配对。本文回顾了实现这一目标的各种数学工具。首先，研究了建立在柱的简化稳态或动态模型上的传统方法，如奇异值分解（SVD）和相对增益阵列（RGA）。研究了优化在控制设计过程系统化中的作用。然后，更近期的推理控制技术依赖于统计方法，如主成分回归（PCR），偏最小二乘回归（PLSR），以及其他机器学习技术，如人工神经网络（ANN）和支持向量机回归（SVMR）进行了广泛的讨论。讨论包括具有复杂结构的新蒸馏技术，如分隔墙塔。最后，介绍了过程模拟器在精馏塔控制结构设计中的应用。
Abstract Design of a control structure in distillation columns involves selecting proper sets of manipulated and controlled variables (often including tray temperatures for inferential control of product compositions) and one-to-one pairing between the two sets. In this paper, various mathematical tools for achieving this goal are reviewed. First, traditional methods such as Singular Value Decomposition (SVD) and Relative Gain Array (RGA) that build upon a simplified steady-state or dynamic model of the column are explored. The role of optimization in systematizing the control design procedures is also investigated. Then, more recent inferential control techniques that rely on statistical methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and other machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine Regression (SVMR) are discussed extensively. The discussions include newer distillation technologies with complex configurations such as dividing-wall columns. Finally, the use of process simulators in aiding the control structure design of distillation columns is surveyed.