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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
- Source :
- Journal of Process Control. 84:171-181
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- 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 (Schafer 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.
- Subjects :
- Economic efficiency
0209 industrial biotechnology
Mathematical optimization
Artificial neural network
Computer science
business.industry
02 engineering and technology
Energy consumption
Industrial and Manufacturing Engineering
Computer Science Applications
Data-driven
Model predictive control
020901 industrial engineering & automation
020401 chemical engineering
Control and Systems Engineering
Modeling and Simulation
Process control
Production (economics)
Electricity
0204 chemical engineering
business
Subjects
Details
- ISSN :
- 09591524
- Volume :
- 84
- Database :
- OpenAIRE
- Journal :
- Journal of Process Control
- Accession number :
- edsair.doi...........4bc929d70fbf955f71046d9d12ef954f