Optimal Design of Laboratory and Pilot‐Plant Experiments Using Multiobjective Optimization

Abstract

Abstract:Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model-based and statistical approaches to optimal design of experiments. The knowledge encoded in the model, is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade-offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

Digitalization in Thermodynamics

“…This is a big advantage compared to having only the information on a single optimal point supplied by the traditional single-objective approach. The MCO approach is general and has also been used in product and process design [13]- [18] and in optimal design of experiments [19].…”

Modeling, Simulation, and Optimization 4.0 for a Distillation Column

“…The combination of sensitivity analysis and multi‐objective optimization could be used to either show the variability of optimization results or for robust (worst case) and stochastic process optimization , . A design of experiments could be used to plan optimal experiments for parameter estimation, when sensitivities – either local based on derivatives or global resulting from a global sensitivity analysis – are available.…”

Multi-criteria optimization for parametrizing excess Gibbs energy models

“…An adaptive sampling algorithm was used here to compute the Pareto front in an efficient manner, following previous works [37][38][39]. The same method has furthermore been applied successfully in previous studies for solving MCO tasks in product and process design [39,[41][42][43][44][45][46], optimal experimental design [47], parameter estimation of molecular simulations [35] and parameter estimation of equations of state [36]. It is an algorithm [37][38][39] that calculates a minimal number of Pareto-optimal solutions and the linear interpolation between them, which approximates the Pareto front within a predefined quality.…”