TY  - JOUR
AU  - Schäfer, Pascal
AU  - Caspari, Adrian
AU  - Mhamdi, Adel
AU  - Mitsos, Alexander
TI  - Economic nonlinear model predictive control using hybrid mechanistic data-driven models for optimal operation in real-time electricity markets: In-silico application to air separation processes
JO  - Journal of process control
VL  - 84
SN  - 0959-1524
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2020-02207
SP  - 171 - 181
PY  - 2019
AB  - Optimization of the energy consumption at fluctuating short-term electricity markets is a promising measure to increase the economic efficiency of energy-intense processes. This can be addressed by integrating the economic perspective directly into the process control, i.e., by using economic nonlinear model predictive control (eNMPC). We present a single-layer eNMPC framework for optimal operation of an industrial-scale nitrogen plant participating in real-time electricity markets. To achieve real-time capability, we utilize suboptimal updates as well as our reduced modeling approach for rectification columns combining compartmentalization and artificial neural networks (Schäfer et al., AIChE J., doi:10.1002/aic.16568). We demonstrate the real-time capability of the approach in-silico. We explicitly account for model-plant mismatch by using a detailed full-order stage-by-stage model that is common in literature as plant replacement. Our results show that close-to-optimal savings in electricity costs are enabled via the eNMPC strategy even under consideration of inherently uncertain market forecasts whilst safely satisfying production targets. Furthermore, the disturbance rejection capability of the control structure is investigated, showing that severe unmeasured disturbances with slow dynamics can be rejected effectively without violating product requirements.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000501410500014
DO  - DOI:10.1016/j.jprocont.2019.10.008
UR  - https://juser.fz-juelich.de/record/877454
ER  -