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This effort is carried out through a series of modeling, experimental, and optimization studies.
The second case study focuses on the combined experimental and optimization of a wheat-based biorefinery that converts wheat into bioethanol, bran-rich pearlings, gluten, and pure yeast cells.
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The proposed concept is tested on an experimental plant and optimization results and exemplary optimal sensor configurations are presented.
In this study, the effect of ethanol diesel blends with different types of additives on performance and exhaust emission is experimentally investigated through experimental modeling and optimization methods.
The experimental design and optimization is accomplished by the design of experimental tool, Response Surface Methodology (RS M.
This workflow, which relies exclusively on experimental observations stored in data repositories, therefore represents an attractive alternative for the prediction of MRM transitions prior to experimental validation and optimization.
A full factorial central composite design was employed for experimental design and optimization of results.
The response surface methodology was used for experimental design and optimization.
This paper describes a value of information method for experimental design and optimization using surrogate modeling.
Then, a constitutive model is proposed for HDPE based on the experimental data and optimization methods.
The investigation used modelling and simulation of the process step, experimental validation and optimization of the simulated process.
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