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Dynamic real-time optimization for a CO2 capture process
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DOI：10.1002/aic.16511.png)
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Growing awareness of climate change has led to increased focus on new energy processes that mitigate generation of CO2, or provide for its efficient capture. To enable the development of these processes, advanced modeling and optimization platforms have been created. New capabilities among platforms include efficient solution strategies for online optimization problems. For real time, dynamic optimization of CO2 capture processes, we demonstrate the application of the Python-based Pyomo platform to facilitate realization of Moving Horizon Estimation and Nonlinear Model Predictive Control, through novel nonlinear optimization and sensitivity strategies. This capability allows large scale, first principle models to be applied for online optimization. Here, we introduce the Control and Adaptation with Predictive Sensitivity Enhancements (CAPRESE) framework to demonstrate this framework for advanced energy processes. Moreover, we present two case studies: profile tracking with detailed tray-by-tray distillation models and Bubbling Fluidized Bed reactors for CO2 capture. (c) 2018 American Institute of Chemical Engineers AIChE J, 65: e16511 2019
Keyword:
fluidization
adsorption
gas
optimization
process control
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4
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1.1W
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2.9W
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