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A methodology for simultaneous design and control for dynamic systems under uncertainty has been developed.
Both studies illustrate the benefits of performing dynamic optimization studies to improve performance for multiscale process systems under uncertainty.
We use the optimization of batch reactive systems under uncertainty as our case study to validate thetheory and methods.
We propose identification-based optimization (IBO) as a novel framework for the optimal design of dynamical systems under uncertainty.
In this work, we introduce a new stochastic multiscenario optimization model for the robust design of desalination systems under uncertainty.
In this paper, a new methodology to integrate process design and control for systems under uncertainty is proposed.
Similar(36)
An inexact two-stage dynamic programming (ITSDP) model has been developed for planning pollutants emission management and electricity and heat supply systems under uncertainties.
This paper presents a new non-probabilistic analysis method for solving the dynamic responses of parametrically excited systems under uncertainties and multi-frequency excitations.
This paper focuses on the following questions: How variant robust SCUC models in terms of different worst-case definitions could impact operational security and economics of power systems under uncertainties?
Numerical case studies compare the performance of the four models and analyze their distinct characteristics for the market economic operation (such as locational marginal prices (LMP), regret costs, and base case costs) and system secure operation (such as load shedding (LS) quantities and Do-Not-Exceed (DNE) limits of power systems under uncertainties.
PCE is subsequently used to perform robust dynamic optimization studies on the catalytic reactor system under uncertainty.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com