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This paper is executed in two stage methodology.
The aim of this work is to present the main features of this production stage methodology.
To properly solve the addressed problem, this work presents a two stage methodology considering social and environment aspects besides the economic performance.
This study makes use of a four stage methodology and those stages are; sequence design, energy comparison, sensitivity comparison, and controllability comparison.
In the first stage of the two stage methodology suggested for optimal in-situ bioremediation design, the optimal number of wells and their locations was determined from preselected candidate well locations.
This work presents a genetic algorithm (GA) based evolution of optimal RBFN architecture and compares its performance with the conventional RBFN training procedure employing a two stage methodology, i.e. utilizing the k-means clustering algorithm for the unsupervised training in the first stage, and using linear supervised techniques for subsequent error minimization in the second stage.
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Further, we expand on this earlier approach by illustrating the differences using several different second stage methodologies.
For this purpose, a two-stage methodology is proposed.
The described three-stage methodology provides the following practical contributions:.
The design of this module followed a two-stage methodology.
First, the three-stage methodology, evaluation criteria and applicable tools are described.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com