Efficient real time optimization using a data-driven piecewise affine model

被引：4

作者：

Yang, Yu ^{[1
]}

Kelly, Jasper ^{[1
]}

机构：

[1] Calif State Univ Long Beach, Dept Chem Engn, Long Beach, CA 90840 USA

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2019年 / 125卷

关键词：

Real time optimization; Dynamic programming; Mixed-integer nonlinear program; Piecewise affine model; SYSTEMS; ADAPTATION; PLANT; IDENTIFICATION;

D O I：

10.1016/j.compchemeng.2019.03.011

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A data-driven real time optimization (RTO) scheme is proposed for complex processes under uncertainties. The objective is to optimize the economic performance of a plant when its first-principle model is difficult to obtain. To this end, a piecewise affine (PWA) model is built by fitting the operating data to predict the production of a process. The trust region of the PWA model is also characterized to ensure reliable predictions. Then, the RTO is formulated as a mixed-integer nonlinear program (MINLP) based on the PWA model. To alleviate the computational demands, we also develop a dynamic programming (DP) scheme that solves the RTO to a near-optimal solution. Two case studies show that the PWA model can accurately forecast the real productions when the process is operated within the trust region, and the proposed DP scheme takes less time to find a better solution than the state-of-the-art solver for the large-scale MINLP. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：545 / 557

页数：13

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