A fuzzy logic model and a neuro-fuzzy system development on supercritical CO2 regeneration of Ni/Al2O3 catalysts

Abstract

Catalyst regeneration is highly important in chemical processes in terms of process performance and economic efficiency. In this study, deactivated rib ring nickel alumina (Ni/Al2O3) catalysts are regenerated in a pilot plant supercritical CO2 extractor. The catalysts are collected from the Iron Midrex reformer unit and are provided by Khouzestan Steel Company (KSC). A Mamdani-type fuzzy logic model and a Neuro-fuzzy system (FIS) model are developed to elucidate the operational parameters i.e. temperature, pressure, and flow rate of supercritical carbon dioxide. The models may define and predict non-experienced data for the output variable, which is the mass concentration of removed deactivating substances from the spent catalyst. Triangular architecture fuzzy models with seventeen rules for the first model, and fourteen rules as the training data for the latter one, are considered to develop the models. The proposed FIS model is validated by using the root mean square error (RMSE) and considering three of the experiments as the checking data. Moreover, the response surface methodology (RSM) is used to find the optimal output value and the related input parameters values. The results show that the proposed neuro-fuzzy model is valid with the RMSE of less than 1.3 %, and the optimum output value found by the RSM is 0.0767, when the temperature, pressure, and flow rate are 57.7, 182.5, and is 0.538, respectively.