Model Parameterization Tailored to Real-Time Optimization
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...
Compressor impellers for mass-market turbochargers are die-casted and machined with an aim to achieve high dimensional accuracy and acquire specific performance. However, manufacturing uncertainties result in dimensional deviations causing incompatible ope ...
Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...
This paper presents the rotor design optimization for a reaction sphere (RS) actuator. The RS is a permanent-magnet synchronous spherical actuator whose rotor is magnetically levitated and can be accelerated about any desired axis. The RS is composed of an ...
Institute of Electrical and Electronics Engineers2014
The management of uncertainties is a challenging task for reliable and secure operation of power systems. The uncertainties come from multiple sources, including the forecast errors of wind power and load, the forced outage of generating units, loss of tra ...
In dynamic optimization problems, the optimal input profiles are typically obtained using models that predict the system behavior. In practice, however, process models are often inaccurate, and on-line adaptation is required for appropriate prediction and ...
In this paper we present a certified reduced basis (RB) framework for the efficient solution of PDE-constrained parametric optimization problems. We consider optimization problems (such as optimal control and optimal design) governed by elliptic PDEs and i ...
In this paper, we propose a model order reduction framework for parametrized quadratic optimization problems constrained by nonlinear stationary PDEs. Once the solutions of the optimization problem are characterized as the solutions of the corresponding op ...
This paper addresses an optimization based approach to follow a geometrically defined path by an unmanned helicopter. In particular, this approach extends reference model following concepts. Instead of using vehicle dynamics, the optimization is based on t ...
A short technical subsection for Gaussian Process learning and Uncertainty propagation is presented as required in applications like Model Predictive Control, machine learning and optimization. ...