Approximating Necessary Conditions of Optimality as Controlled Variables

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• 作者：Lingjian Ye ; Yi Cao ; Yingdao Li ; Zhihuan Song
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2013
• 出版时间：January 16, 2013
• 年：2013
• 卷：52
• 期：2
• 页码：798-808
• 全文大小：425K
• 年卷期：v.52,no.2(January 16, 2013)
• ISSN：1520-5045

文摘

Selection of controlled variables (CVs) has recently gained wide attention, because of its paramount importance in real-time optimization (RTO) of plant operation. The so-called self-optimizing control (SOC) strategy aims to select appropriate CVs so that when they are maintained at constant setpoints, the overall plant operation is optimal or near optimal, despite various disturbances and uncertainties. Recent progresses of the SOC methodology have focused on finding linear combinations of measurements as CVs via linearization of the process around its nominal operating point, which results in the plant operation being only locally optimal. In this work, the concept of necessary conditions of optimality (NCO) is incorporated into CV selection to overcome the 鈥渓ocal鈥?shortcoming of existing SOC methods. Theoretically, the NCO should be selected as the optimal CV, although it may not be practical because of the measurability of the NCO. To address this issue, in this work, CVs are selected to approximate unmeasured NCO over the entire operation region with zero setpoints to achieve near-optimal operation globally. The NCO approximation CVs can be obtained through any existing regression approaches. Among them, two particular regression methods鈥攏amely, least-squares and neural networks鈥攁re adopted in this work as an illustration of the proposed methodology. The effectiveness and advantages of the new approach are demonstrated through two case studies. Results are compared with those obtained by using existing SOC methods and an NCO tracking technique.