Dynamic optimization and nonlinear model predictive control of a semi-batch epoxidation process

被引：5

作者：

Joy, Preet ^{[1
]}

Schultz, Eduardo S. ^{[1
]}

Ebrahimi, Fatemeh ^{[1
]}

Turan, Umut ^{[1
]}

Casteel, Steffen ^{[1
]}

Schaffrath, Thomas ^{[1
]}

Hammen, Rupert ^{[1
]}

Mhamdi, Adel ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Aachener Verfahrenstech Proc Syst Engn SVT, Forckenbeckstr 51, D-52074 Aachen, Germany

来源：

JOURNAL OF PROCESS CONTROL | 2021年 / 108卷

关键词：

Dynamic optimization; NMPC; DRTO; Raman spectroscopy; Epoxidation; REAL-TIME OPTIMIZATION; EMULSION POLYMERIZATION; RAMAN-SPECTROSCOPY;

D O I：

10.1016/j.jprocont.2021.10.013

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We investigate offline dynamic optimization (ODO) and multi-layer control schemes to optimally operate the epoxidation of oleic acid. The process involves a two-phase reaction mixture with highly exothermic reactions wherein the reactor temperature needs to be controlled to guarantee safe operation. Optimal operating conditions are computed using ODO and the control scheme is responsible to enforce such conditions. We compare a conventional control scheme, which uses PI controllers, against two multi-layer control schemes: (i) using nonlinear model predictive control (NMPC) and (ii) using dynamic real-time optimization (DRTO) together with NMPC. With ODO and NMPC we achieve a reduction in process time greater than 75%, compared to a standard process, while still maintaining safe operating conditions. With DRTO and NMPC, the same reduction in process time is obtained, however with a maximum violation of the temperature constraint by 12%. This may be due to model-plant mismatch and large computational time required for the optimization. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页码：55 / 67

页数：13