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- W2995435061 abstract "Dry reforming of methane is a promising method to reduce the emission of CO2 and to use it in various type of Fischer–Tropsch synthesis and production of syngas. In order to obtain desirable products efficiently, the effect of reactants on the products must be known precisely. For this purpose, several studies have published for modeling the dry reforming of methane process with artificial intelligence-based data-driven prediction models. Due to lack of investigating overfitting problem and deficient and/or biased performance evaluations, actual potential of proposed methods have not been revealed for predicting certain outputs of the process. In this paper, we employed three regression methods and developed prediction models using a dataset with 57 observations. Performance evaluations of the models are performed with 10-fold cross-validation to ensure unbiased results. Proposed methods’ both training and testing performances are separately investigated, further applicability is discussed." @default.
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- W2995435061 date "2020-03-31" @default.
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- W2995435061 title "İstatistiksel ve Makine Öğrenmeye Dayalı Yaklaşımlarla Kobalt Katalizör Üzerinden Metan Kuru Reformundan Elde Edilen Sentez Gazının Tahmini Modellemesi" @default.
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- W2995435061 doi "https://doi.org/10.7240/jeps.558373" @default.
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