期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 113, 期 -, 页码 139-151出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2018.03.009
关键词
Scheduling of production; Process control; Integrated scheduling and control; Model predictive control; Air separation units
资金
- NSF [CBET 1159244]
- CNPQ - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - Brazil
- National Science Foundation (NSF) through the CAREER Award [1454433, CBET-1512379]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1512379, 1159244] Funding Source: National Science Foundation
The integration of dynamic process models in scheduling calculations has recently received significant attention as a mean to improve operational performance in increasingly dynamic markets. In this work, we propose a novel framework for the integration of scheduling and model predictive control (MPC), which is applicable to industrial size problems involving fast changing market conditions. The framework consists on identifying scheduling-relevant process variables, building low-order dynamic models to capture their evolution, and integrating scheduling and MPC by, (i) solving a simulation-optimization problem to define the optimal schedule and, (ii) tracking the schedule in closed-loop using the MPC controller. The efficacy of the framework is demonstrated via a case study that considers an air separation unit operating under real-time electricity pricing. The study shows that significant cost reductions can be achieved with reasonable computational times. (C) 2018 Elsevier Ltd. All rights reserved.
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