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RSM is an approach that uses a philosophy of sequential experimentation with the ultimate goal of optimizing a process while using available experimentation resources efficiently.
Because of the fierce competition in emerging technology platforms, the real differentiation in the platforms may come down to a very low-level technology feature (e.g., a particular algorithm for optimizing a process embodied in source code) or abstract concepts embodied in the form of trade secrets.
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Modern methods are optimization-based, using for example Mixed Integer Non-Linear Programming (MINLP) formulation to optimize a process superstructure.
The problem of how to optimize a process based on many parameters can be likened to locating the peak of a mountain range.
In our theoretical example, for instance, it was possible to optimize a process so that all of the X's were moved close to or below the diagonal.
In this paper, we optimize a process that integrates the use of glycerol to produce ethanol via fermentation within the simultaneous production of biodiesel and bioethanol from algae.
You have to establish, measure and optimize a process.
Response surface methodology is a statistical procedure used to understand, improve, and optimize a process.
Other methodologies, in the form of algorithms, may be used to optimize a process.
To optimize a process like the photocatalytic degradation process, it is essential to study all factors influencing the process.
In a professional practice, one may want to test or optimize a process, while in a teaching practice, students have to learn something.
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