30 citations · 35 across the 8 of their papers we have counts for
6 papers · 1 filter
An adaptive discretization algorithm for locally optimal experimental design with constraints
Jochen Schmid, Philipp Seufert, Jan Schwientek +2
We develop a novel iterative algorithm for locally optimal experimental design under constraints, like budget or performance constraints. It is an adaptive discretization algorithm…
On Computing and Pricing of Adjustable Robust Chemical Process Designs
Jan Schwientek, Katrin Teichert, Jan Schröder +6
Model-based process simulation can be used to derive designs and operating conditions of chemical processes that optimally balance multiple objectives, such as quality, costs, or e…
Pareto-optimal treatment of uncertainties in model-based process design and operation
Jan Schwientek, Katrin Teichert, Jan Schröder +5
Model-based process design and operation involves here-and-now and wait-and-see decisions. Here-and-now decisions include design variables like the size of heat exchangers or the h…
Multicriteria Adjustable Robustness
Elisabeth Halser, Elisabeth Finhold, Neele Leithäuser +3
Multicriteria adjustable robust optimization (MARO) problems arise in a wide variety of practical settings, for example, in the design of a building's energy supply. However, no ge…
Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
David Mogalle, Philipp Seufert, Jan Schwientek +2
Modeling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested,…
Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones?
Charlie Vanaret, Philipp Seufert, Jan Schwientek +5
Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, p…