Predictive Modeling of the Syngas Production fromMethane Dry Reforming over Cobalt Catalyst with Statistical and Machine Learning Based Approaches

Abstract

Dry reforming of methane is a promising method to reduce the emission of CO2and to use it in various type of Fischer–Tropschsynthesis 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 withartificial 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, i.e., artificial neural networks, support vector machine and polynomial regressiontodevelop 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.